Triple
T5776589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Football League |
E127456
|
entity |
| Predicate | playedFirstChampionshipGame |
P66387
|
FINISHED |
| Object | 1960 |
—
|
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: 1960 | Statement: [American Football League, playedFirstChampionshipGame, 1960]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedFirstChampionshipGame Context triple: [American Football League, playedFirstChampionshipGame, 1960]
-
A.
playsChampionshipGame
Indicates that an entity participates as a competitor in a championship-level game or match.
-
B.
hasChampionshipGames
Indicates that an entity includes, hosts, or is associated with one or more championship-level games or matches.
-
C.
firstGamePlayed
Indicates the specific game that an entity participated in before any other, marking the earliest game in which it played.
-
D.
wonChampionshipAsPlayerWith
Indicates that one entity won a championship while playing on a team or alongside the other entity.
-
E.
careerGamesStarted
Indicates the total number of games an entity has started over the course of its entire career.
- 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_69c008361fa88190aefa4dc41b051e7f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02acb12c081908e4beee4a957f9f9 |
completed | March 22, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69c021d0c6088190ba670ddcdbf5ca3e |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c02ac9603481909e3fa295d7904a15 |
completed | March 22, 2026, 5:45 p.m. |
Created at: March 22, 2026, 3:50 p.m.