Triple
T22745731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crosstown Shootout |
E562547
|
entity |
| Predicate | hasWomenSeries |
P149585
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Crosstown Shootout, hasWomenSeries, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWomenSeries Context triple: [Crosstown Shootout, hasWomenSeries, true]
-
A.
hasWomenTeamInLeague
Indicates that an entity has a women’s team that participates in a specified league.
-
B.
hasWomenTeamPlan
Indicates that an entity offers or is associated with a specific plan or program designed for women’s teams.
-
C.
hasFemaleCompetitors
Indicates that an entity participates in a competitive context where at least some of the competitors are female.
-
D.
hasWomenChampionship
Indicates that an entity organizes, hosts, or is associated with a championship specifically for women.
-
E.
hasWomenRace
Indicates that an entity includes, organizes, or is associated with a race event specifically for women.
- 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_69e245513a5c81908d5cb471b4fc429d |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f179b563e481908804d07fca1777b9 |
completed | April 29, 2026, 3:23 a.m. |
| PD | Predicate disambiguation | batch_69eed2b88d88819096015deb6a648801 |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:23 p.m.