Triple
T9508025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luke Harding |
E229319
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win
"Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win" is an investigative nonfiction book by journalist Luke Harding that explores alleged ties between Donald Trump’s 2016 presidential campaign and Russian officials and oligarchs.
|
E804201
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win | Statement: [Luke Harding, notableWork, Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win Context triple: [Luke Harding, notableWork, Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win]
-
A.
Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth
"Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth" is a nonfiction book by Rachel Maddow that investigates how the global oil and gas industry fuels corruption, undermines democracy, and destabilizes international politics.
-
B.
The Comey Rule
The Comey Rule is a political drama miniseries that dramatizes former FBI Director James Comey’s interactions with Donald Trump and the events surrounding the 2016 U.S. presidential election.
-
C.
Revenge: How Donald Trump Weaponized the US Department of Justice Against His Critics
"Revenge: How Donald Trump Weaponized the US Department of Justice Against His Critics" is a political memoir and exposé by Michael Cohen alleging that Donald Trump abused federal law enforcement to target his opponents.
-
D.
Raising Trump
Raising Trump is a memoir by Ivana Trump in which she recounts her life, marriage to Donald Trump, and experiences raising their three children.
-
E.
We Steal Secrets: The Story of WikiLeaks
We Steal Secrets: The Story of WikiLeaks is a documentary film that examines the rise of WikiLeaks, its controversial disclosures, and the roles of Julian Assange and Chelsea Manning in reshaping debates over secrecy and transparency.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win Triple: [Luke Harding, notableWork, Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win]
Generated description
"Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win" is an investigative nonfiction book by journalist Luke Harding that explores alleged ties between Donald Trump’s 2016 presidential campaign and Russian officials and oligarchs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win Target entity description: "Collusion: Secret Meetings, Dirty Money, and How Russia Helped Donald Trump Win" is an investigative nonfiction book by journalist Luke Harding that explores alleged ties between Donald Trump’s 2016 presidential campaign and Russian officials and oligarchs.
-
A.
Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth
"Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth" is a nonfiction book by Rachel Maddow that investigates how the global oil and gas industry fuels corruption, undermines democracy, and destabilizes international politics.
-
B.
The Comey Rule
The Comey Rule is a political drama miniseries that dramatizes former FBI Director James Comey’s interactions with Donald Trump and the events surrounding the 2016 U.S. presidential election.
-
C.
Revenge: How Donald Trump Weaponized the US Department of Justice Against His Critics
"Revenge: How Donald Trump Weaponized the US Department of Justice Against His Critics" is a political memoir and exposé by Michael Cohen alleging that Donald Trump abused federal law enforcement to target his opponents.
-
D.
Raising Trump
Raising Trump is a memoir by Ivana Trump in which she recounts her life, marriage to Donald Trump, and experiences raising their three children.
-
E.
We Steal Secrets: The Story of WikiLeaks
We Steal Secrets: The Story of WikiLeaks is a documentary film that examines the rise of WikiLeaks, its controversial disclosures, and the roles of Julian Assange and Chelsea Manning in reshaping debates over secrecy and transparency.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca847611c48190a28c028644198c75 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9855c5e48190a7d8d39b6d601679 |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d13a2b34948190826b1f58258a4f54 |
completed | April 4, 2026, 4:19 p.m. |
| NEDg | Description generation | batch_69d13bc8ce4081909a58db4014f2748d |
completed | April 4, 2026, 4:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d13c58beb08190ab41485bc7dd9b6d |
completed | April 4, 2026, 4:29 p.m. |
Created at: March 30, 2026, 7:57 p.m.