Triple
T36128243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Bates |
E1044942
|
entity |
| Predicate | relatedLegalEvent |
P40672
|
FINISHED |
| Object | John Bates’s arrest |
—
|
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: John Bates’s arrest | Statement: [Vera Bates, relatedLegalEvent, John Bates’s arrest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedLegalEvent Context triple: [Vera Bates, relatedLegalEvent, John Bates’s arrest]
-
A.
relatedLegalProcess
chosen
Indicates that there is an associated legal proceeding or action that is connected to, arises from, or is otherwise relevant to the referenced entity or event.
-
B.
legalStatusEvent
Indicates an event or action that changes, establishes, or affects the legal status or standing of an entity.
-
C.
judicialEvent
Indicates a relationship where an event involves judicial or court-related proceedings, such as trials, hearings, or legal rulings.
-
D.
legalCaseRelatedTo
Indicates that there is a relevant connection or association between a legal case and another entity, such as a person, organization, event, or legal matter.
-
E.
associatedCourtCase
Indicates a relationship where one entity is linked to, or involved in, a particular court case.
- 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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:08 p.m.