Triple
T5963757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rookie of the Year Award |
E132701
|
entity |
| Predicate | firstYearWithSeparateALNLWinners |
P124
|
FINISHED |
| Object | 1949 |
—
|
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: 1949 | Statement: [Rookie of the Year Award, firstYearWithSeparateALNLWinners, 1949]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstYearWithSeparateALNLWinners Context triple: [Rookie of the Year Award, firstYearWithSeparateALNLWinners, 1949]
-
A.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
-
B.
firstAwarded
chosen
Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
-
C.
initialAwardYear
Indicates the year in which an entity first received a particular award.
-
D.
hasNotableFirstFemaleWinnerYear
Indicates the year in which the first notable female winner associated with an entity achieved her win.
-
E.
wasInitiallySingleLeagueAward
Indicates that an award was originally given within a single unified league before any later league splits or structural changes.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03fb7f8a88190a8bd45208bda4a03 |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0335a635881909c58c1ef0f97f1e8 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:03 p.m.