Triple
T4364604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jermaine Lewis |
E98741
|
entity |
| Predicate | scoredTouchdownType |
P55791
|
FINISHED |
| Object | kickoff return touchdown |
—
|
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: kickoff return touchdown | Statement: [Jermaine Lewis, scoredTouchdownType, kickoff return touchdown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredTouchdownType Context triple: [Jermaine Lewis, scoredTouchdownType, kickoff return touchdown]
-
A.
decisiveTouchdownScorer
Indicates the player who scored the touchdown that ultimately determined the outcome of the game.
-
B.
GiantsOnlyTouchdownType
Indicates that the specified type of touchdown was scored exclusively by the Giants and not by any other team.
-
C.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
D.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
E.
gameWinningScoreType
Indicates the type or category of score (e.g., goal, point, run) that results in winning a game.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351fea3e88190807b99868982c491 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff654308190b9717526120d80d3 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:16 p.m.