Triple
T275924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woody Johnson |
E5249
|
entity |
| Predicate | appointedBy |
P257
|
FINISHED |
| Object | Donald Trump |
E765
|
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: Donald Trump | Statement: [Woody Johnson, appointedBy, Donald Trump]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donald Trump Context triple: [Woody Johnson, appointedBy, Donald Trump]
-
A.
Donald Trump
chosen
Donald Trump is an American businessman, television personality, and politician who served as the 45th president of the United States from 2017 to 2021.
-
B.
Fred Trump
Fred Trump was an American real estate developer who built a large portfolio of middle-income housing in New York City and was a prominent figure in the city's mid-20th-century real estate industry.
-
C.
Clinton
Clinton is a small town in Dutchess County, New York, known for its rural character and historic Hudson Valley setting.
-
D.
Johnson
Johnson is a common English surname borne by numerous notable individuals across politics, arts, sports, and other fields.
-
E.
Trump Hotels
Trump Hotels is a luxury hotel brand associated with former U.S. President Donald Trump, known for its upscale properties and high-end hospitality services.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25dec53ac8190912f3d79576131fa |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3cfe55c4c81908521c5161f0d844b |
completed | March 1, 2026, 5:34 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.