Triple
T36308013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mauritius Turf Club |
E893992
|
entity |
| Predicate | usesRacecourse |
P157863
|
FINISHED |
| Object | Champ de Mars Racecourse |
—
|
NE NERFINISHED |
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: Champ de Mars Racecourse | Statement: [Mauritius Turf Club, usesRacecourse, Champ de Mars Racecourse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRacecourse Context triple: [Mauritius Turf Club, usesRacecourse, Champ de Mars Racecourse]
-
A.
racecourseUsed
chosen
Indicates that a particular racecourse is utilized or employed for a given event, activity, or purpose.
-
B.
hasRacecourse
Indicates that an entity possesses, contains, or is associated with a racecourse facility or track.
-
C.
racecourseFeature
Indicates that one entity is a physical or functional feature or component of a racecourse associated with the other entity.
-
D.
racecourseType
Indicates the specific kind or classification of a racecourse associated with an entity.
-
E.
hasRacecourseFeature
Indicates that something possesses or includes a specific feature or characteristic related to a racecourse.
- 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_69f76e4c1b248190b10667d0213537fe |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:09 p.m.