Triple
T36917079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Tech vs Michigan State |
E913071
|
entity |
| Predicate | MichiganStateLeadingScorerPoints |
P149560
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Texas Tech vs Michigan State, MichiganStateLeadingScorerPoints, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MichiganStateLeadingScorerPoints Context triple: [Texas Tech vs Michigan State, MichiganStateLeadingScorerPoints, 16]
-
A.
Michigan StateLeadingScorer
Indicates that the subject is the leading scorer for the Michigan State team in a given season or context.
-
B.
Michigan StateScore
chosen
Indicates that a specified score is associated with Michigan State in a particular game or event.
-
C.
MichiganStateFieldGoalPercentage
Indicates the percentage of field goal attempts successfully made by Michigan State in a given context (e.g., game or season).
-
D.
collegeTeamPointsLeader
Indicates that the subject is the player who has scored the most points for a particular college team.
-
E.
mostPointsPerGamePlayer
Indicates the player who has the highest average number of points scored per game within a given context or season.
- F. None of above.
Provenance (3 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_69f76e885b848190bad82c87e9525486 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fbaebc8f2c8190b94f1b4a3ec92e8c |
completed | May 6, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69fbadf1e6008190a71bbd196ba06844 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:13 p.m.