Triple
T1438039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All the President’s Men |
E31000
|
entity |
| Predicate | academyAwardWin |
P10689
|
FINISHED |
| Object | Best Supporting Actor (Jason Robards) |
—
|
LITERAL 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: Best Supporting Actor (Jason Robards) | Statement: [All the President’s Men, academyAwardWin, Best Supporting Actor (Jason Robards)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academyAwardWin Context triple: [All the President’s Men, academyAwardWin, Best Supporting Actor (Jason Robards)]
-
A.
academyAwardWins
Indicates that one entity has won a specified number of Academy Awards (Oscars) or that a winning relationship exists between the entity and the Academy Award.
-
B.
oscarAward
chosen
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
C.
bestPictureWinner
Indicates that the subject is the film that won the Best Picture award in a given context or year.
-
D.
academyAwardForBestActress
Indicates that an entity received the Academy Award for Best Actress in a leading role.
-
E.
bestActorWinner
Indicates that the subject is the recipient of a "Best Actor" award for a particular performance or event.
- F. None of above.
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_69a4991633388190a4d61b5a98aa407a |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5ff8dbc81909eafcfc9f2260a22 |
completed | March 1, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69a4c478f65481909ee716791c663491 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.