Triple
T5963756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rookie of the Year Award |
E132701
|
entity |
| Predicate | firstYearWithTwoLeagueWinners |
P67089
|
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, firstYearWithTwoLeagueWinners, 1949]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstYearWithTwoLeagueWinners Context triple: [Rookie of the Year Award, firstYearWithTwoLeagueWinners, 1949]
-
A.
firstSeasonChampions
Indicates that the subject entity won the championship in the first season of the relevant competition or series.
-
B.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
-
C.
secondPremiershipYear
Indicates the year in which an entity held or achieved its second term of premiership.
-
D.
firstSeasonBothFranchisesPlayed
Indicates the first season in which both franchises were active and played concurrently.
-
E.
mostWinsYears
Indicates the years during which an entity achieved the highest number of wins compared to others or compared to its own performance in other years.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69c03fb6bb7c81909c5629fba408dc69 |
completed | March 22, 2026, 7:15 p.m. |
Created at: March 22, 2026, 4:03 p.m.