Triple
T965428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trump World Tower |
E20826
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Trump |
E52455
|
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: Trump | Statement: [Trump World Tower, brand, Trump]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trump Context triple: [Trump World Tower, brand, Trump]
-
A.
Trump
chosen
Trump is a prominent American surname most widely associated with businessman and former U.S. President Donald Trump and his family.
-
B.
Donald Trump
Donald Trump is an American businessman, television personality, and politician who served as the 45th president of the United States from 2017 to 2021.
-
C.
Clinton
Clinton is a prominent American political surname most famously associated with Hillary Clinton, the former U.S. Secretary of State, senator, and First Lady.
-
D.
Clinton
Clinton is a small city in eastern Tennessee that serves as the county seat of Anderson County and is part of the greater Knoxville region.
-
E.
Clinton
Clinton is a small town in Dutchess County, New York, known for its rural character and historic Hudson Valley setting.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b431d61481908b53490e99670363 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4287830c819095ffc30fc03a7461 |
completed | March 7, 2026, 3:21 p.m. |
Created at: March 1, 2026, 7:40 p.m.