Triple
T10908761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F-3 |
E257637
|
entity |
| Predicate | partOfLineup |
P96347
|
FINISHED |
| Object | Ford Bonus-Built trucks |
—
|
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: Ford Bonus-Built trucks | Statement: [F-3, partOfLineup, Ford Bonus-Built trucks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLineup Context triple: [F-3, partOfLineup, Ford Bonus-Built trucks]
-
A.
positionInLineup
Indicates the specific ordered place an entity occupies within a lineup or sequence of entities.
-
B.
playsInPosition
Indicates that an entity (typically a player) performs or operates in a specific role or position within a game, sport, or activity.
-
C.
positionPlayedForTeam
Indicates the specific playing position or role an individual held while participating on a particular team.
-
D.
memberOfSportsTeamAsPlayer
Indicates that a person participates as a player on a specific sports team.
-
E.
starPlayerOnChampionTeam
Indicates that a player is the standout or key performer on a team that has won a championship.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7706824d08190ba894d144cc6b3ba |
completed | April 9, 2026, 9:24 a.m. |
| PD | Predicate disambiguation | batch_69d70d3d69e08190bb369e9a7927142c |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101de31c819090707635f6790559 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:22 p.m.