Triple
T728271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Picture |
E14775
|
entity |
| Predicate | ceremonyNumberOfFirstAward |
P8124
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Academy Award for Best Picture, ceremonyNumberOfFirstAward, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ceremonyNumberOfFirstAward Context triple: [Academy Award for Best Picture, ceremonyNumberOfFirstAward, 1]
-
A.
firstCeremony
Indicates that an entity participates in or is associated with the earliest or initial ceremony in a sequence or context.
-
B.
firstAwarded
Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
-
C.
ceremonyNumber
chosen
Indicates the specific ordinal or identifying number assigned to a particular ceremony within a set or sequence of ceremonies.
-
D.
numberOfAwards
Indicates the total count of awards that have been received by an entity.
-
E.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64adf2c81908e48090be35dd9d9 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f839608190878a60eb7a044ed9 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.