Triple
T31303537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1981 Brixton riot |
E798271
|
entity |
| Predicate | numberOfPoliceInjured |
P50024
|
FINISHED |
| Object | over 280 |
—
|
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: over 280 | Statement: [1981 Brixton riot, numberOfPoliceInjured, over 280]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPoliceInjured Context triple: [1981 Brixton riot, numberOfPoliceInjured, over 280]
-
A.
casualtiesPoliceInjured
chosen
Indicates that the event resulted in police officers being injured.
-
B.
numberOfOfficersInvolved
Indicates the total count of officers who participated in or were involved in a particular event or action.
-
C.
hasNumberOfPoliceOfficersKilled
Indicates the number of police officers who were killed in relation to a specific event, situation, or context.
-
D.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
E.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
- 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_69f224e0bd4c8190aab9b29a73f7aa3c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00ccadb6908190ab810a7c05315fa8 |
completed | May 10, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_6a00cc0f86b88190a0d2c43618558f86 |
completed | May 10, 2026, 6:18 p.m. |
Created at: April 29, 2026, 9:14 p.m.