Triple
T27869298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chester Assizes |
E704446
|
entity |
| Predicate | handledOffenceType |
P67290
|
FINISHED |
| Object | murder |
—
|
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: murder | Statement: [Chester Assizes, handledOffenceType, murder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: handledOffenceType Context triple: [Chester Assizes, handledOffenceType, murder]
-
A.
definesOffence
Indicates that one entity specifies or establishes the nature, elements, or scope of an offence associated with another entity.
-
B.
includesOffenseType
chosen
Indicates that one entity contains, specifies, or is associated with a particular category or type of offense.
-
C.
targetOffenderType
Indicates the specific category or type of offender that an action, rule, or condition is directed toward.
-
D.
offenceCognizable
Indicates that the offence is of a type for which police may initiate investigation and arrest without requiring prior permission or a warrant from a magistrate.
-
E.
offensiveMisplayType
Indicates the specific kind of mistake or error committed by the offensive side during a play 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_69ef840f12408190b539d00d79658abf |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f65a6c900881908f18b61273d7bf8d |
completed | May 2, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69f659ce58408190ba9e007b4810d4d0 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 27, 2026, 6:23 p.m.