Triple
T7265225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyne–Wear derby |
E159753
|
entity |
| Predicate | notableIncidentType |
P76024
|
FINISHED |
| Object | crowd trouble |
—
|
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: crowd trouble | Statement: [Tyne–Wear derby, notableIncidentType, crowd trouble]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableIncidentType Context triple: [Tyne–Wear derby, notableIncidentType, crowd trouble]
-
A.
notableProtectiveIncident
Indicates that a significant event occurred in which one entity protected or defended another in a notable or remarkable way.
-
B.
notableViolentCase
Indicates that an entity is associated with a specific case or incident that is recognized as involving significant or noteworthy violence.
-
C.
notableVictim
Indicates that the subject is a person or entity who is notably recognized as a victim of the object (such as an event, crime, or harmful action).
-
D.
notableDisasterType
Indicates the specific kind or category of disaster for which something (such as a place, event, or entity) is notable or best known.
-
E.
notableEncounterWith
Indicates that an entity has had a significant or noteworthy meeting, interaction, or confrontation with another entity.
- 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_69c68838f9948190875fd60b2351230c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb088dac8190b353f6ea3d686025 |
completed | March 27, 2026, 8:39 p.m. |
| PD | Predicate disambiguation | batch_69c6e76876608190ac4652bc7153302e |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6eb06acd881908a1735fbbbd5756f |
completed | March 27, 2026, 8:39 p.m. |
Created at: March 27, 2026, 2:57 p.m.