Triple
T38670634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Point-a-Minute teams |
E940575
|
entity |
| Predicate | totalPointsAllowed |
P193287
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [Point-a-Minute teams, totalPointsAllowed, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalPointsAllowed Context triple: [Point-a-Minute teams, totalPointsAllowed, 40]
-
A.
totalPointsAvailable
Indicates the complete number of points that can be obtained or assigned within a given context or activity.
-
B.
totalPointsScored
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
-
C.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
D.
touchdownPoints
Indicates the number of points awarded to a team for successfully scoring a touchdown in a game.
-
E.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
- 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_69f76edfde348190bf6529d9f49ecd62 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
| PDg | Predicate description generation | batch_69fd3d45ccb8819082f15e60bd33afc9 |
completed | May 8, 2026, 1:32 a.m. |
Created at: May 3, 2026, 4:33 p.m.