Triple
T5870449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steel City derby |
E130501
|
entity |
| Predicate | policingCategory |
P21773
|
FINISHED |
| Object | high-risk football fixture |
—
|
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: high-risk football fixture | Statement: [Steel City derby, policingCategory, high-risk football fixture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policingCategory Context triple: [Steel City derby, policingCategory, high-risk football fixture]
-
A.
officerCategory
Indicates the classification or type of officer role that an individual holds within an organization or system.
-
B.
lawEnforcementLabel
Indicates that an entity has been designated, tagged, or classified by a law enforcement authority for monitoring, identification, or investigative purposes.
-
C.
policeSystem
Indicates a relationship where an entity functions as, belongs to, or is governed by a system of law enforcement or policing.
-
D.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
E.
policingModel
chosen
Indicates the approach, strategy, or framework used to organize and conduct policing activities or law enforcement operations.
- 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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ffaef081909faaa7f420a3b9b7 |
completed | March 22, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69c03347e51c81909053bcf34e3b88ab |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:56 p.m.