Triple
T10194929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luke Monaghan |
E238137
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Luke Monaghan |
E238137
|
NE FINISHED |
How this triple was built (2 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: Luke Monaghan | Statement: [Luke Monaghan, name, Luke Monaghan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luke Monaghan Context triple: [Luke Monaghan, name, Luke Monaghan]
-
A.
Luke Monaghan
chosen
Luke Monaghan is the brother of actor Dominic Monaghan, known for his work behind the camera as a director and filmmaker.
-
B.
Liam Mellows
Liam Mellows was an Irish republican leader and revolutionary who played a prominent role in both the Easter Rising of 1916 and the Irish Civil War.
-
C.
Dylan McLaughlin
Dylan McLaughlin is an American former child actor best known for his roles in family comedies and dramas in the early 2000s.
-
D.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
E.
Jared Vennett
Jared Vennett is a slick, opportunistic Wall Street trader in "The Big Short" who profits by betting against the U.S. housing market before its 2008 collapse.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc7cc748190bceb8f657afcc054 |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d35512ab2c8190b2802c7bb22e7323 |
completed | April 6, 2026, 6:39 a.m. |
Created at: March 30, 2026, 9:13 p.m.