Triple
T26936002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellis (uncle) |
E678383
|
entity |
| Predicate | relationshipToLaw |
P142158
|
FINISHED |
| Object | former peace officer |
—
|
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: former peace officer | Statement: [Ellis (uncle), relationshipToLaw, former peace officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLaw Context triple: [Ellis (uncle), relationshipToLaw, former peace officer]
-
A.
relationshipToStateLaw
Indicates how something is connected or subject to the provisions, requirements, or authority of a particular state law.
-
B.
hasRelationshipToLaw
chosen
Indicates that one entity has a defined connection, relevance, or applicability to a specific law or legal framework.
-
C.
relationshipToTribalLaw
Indicates that one entity has a specified legal or regulatory connection, status, or relevance with respect to tribal law.
-
D.
relationshipToBond
Indicates the specific type of personal, familial, or professional relationship an entity has to the person named Bond.
-
E.
relationshipToState
Indicates a relationship or connection that an entity has with a particular state or governmental body.
- 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_69eeeb4cac908190a45956c2993d1cc2 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69ff069ec1348190815375c5c9e38404 |
completed | May 9, 2026, 10:04 a.m. |
| PD | Predicate disambiguation | batch_69ff05ba57f88190a45d20f18044e0fb |
completed | May 9, 2026, 10 a.m. |
Created at: April 27, 2026, 6:15 a.m.