Triple
T22490341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Country derby |
E555998
|
entity |
| Predicate | hasPolicePresence |
P80479
|
FINISHED |
| Object | heightened on matchdays |
—
|
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: heightened on matchdays | Statement: [Black Country derby, hasPolicePresence, heightened on matchdays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolicePresence Context triple: [Black Country derby, hasPolicePresence, heightened on matchdays]
-
A.
policePresence
chosen
Indicates that law enforcement officers are present at or monitoring a particular location, event, or situation.
-
B.
hasPoliceInstitution
Indicates that an entity is associated with, governed by, or served by a particular police institution or law enforcement body.
-
C.
hasPoliceDepartment
Indicates that an entity possesses, is served by, or is administratively associated with a police department.
-
D.
hasPolicePartner
Indicates that one entity has another entity as its partner in a police or law-enforcement context.
-
E.
hasReservationPolice
Indicates that an entity has an official reservation or booking specifically with the police or a police-related authority.
- 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c40930c81908310ac6afd98c62e |
completed | April 29, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69e898b6eee08190ba673a0ee329e671 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:49 p.m.