Triple
T7718882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Otto Graham |
E174955
|
entity |
| Predicate | gamesStartedInChampionships |
P78328
|
FINISHED |
| Object | 10 consecutive league championship games |
—
|
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: 10 consecutive league championship games | Statement: [Otto Graham, gamesStartedInChampionships, 10 consecutive league championship games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gamesStartedInChampionships Context triple: [Otto Graham, gamesStartedInChampionships, 10 consecutive league championship games]
-
A.
hasChampionshipGames
Indicates that an entity includes, hosts, or is associated with one or more championship-level games or matches.
-
B.
playedFirstChampionshipGame
Indicates that an entity participated in its first championship game in a given sport, league, or competition.
-
C.
playsChampionshipGame
Indicates that an entity participates as a competitor in a championship-level game or match.
-
D.
gamesStartedNFL
Indicates that the subject has started (been in the starting lineup for) one or more games in the National Football League.
-
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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ebb7448190ae8d47fe0cbb0907 |
completed | March 27, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69c701683dec8190be9861e592aa8ce0 |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c702e9a32081909a153190a62af426 |
completed | March 27, 2026, 10:21 p.m. |
Created at: March 27, 2026, 4:05 p.m.