Triple
T10518522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1987 NFL season |
E248100
|
entity |
| Predicate | replacementGamesCount |
P94352
|
FINISHED |
| Object | 3 weeks |
—
|
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: 3 weeks | Statement: [1987 NFL season, replacementGamesCount, 3 weeks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: replacementGamesCount Context triple: [1987 NFL season, replacementGamesCount, 3 weeks]
-
A.
numberOfReleasedGames
Indicates the total count of games that have been released by or associated with a given entity.
-
B.
relatedGame
Indicates that one game has a notable connection or association with another game, such as shared content, themes, or series.
-
C.
gamesPlayed
Indicates the number or set of games that an entity has participated in or completed.
-
D.
numberOfTableGames
Indicates the quantity of table games associated with or available in relation to a given entity.
-
E.
typicalNumberOfGamesRange
Indicates the usual minimum and maximum number of games typically played in the associated context or setting.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509dd29b48190aa5b170e2558545c |
completed | April 7, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69d4fb94fa10819091f585bab4379c6f |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe06d4a48190b1a45dd1d4e16df0 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:28 p.m.