Triple
T20864713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrick Martin |
E513721
|
entity |
| Predicate | hasRelationshipToLaw |
P142158
|
FINISHED |
| Object | officer of the court |
—
|
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: officer of the court | Statement: [Patrick Martin, hasRelationshipToLaw, officer of the court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToLaw Context triple: [Patrick Martin, hasRelationshipToLaw, officer of the court]
-
A.
relationshipToStateLaw
Indicates how something is connected or subject to the provisions, requirements, or authority of a particular state law.
-
B.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
C.
containsLaw
Indicates that one entity (such as a document, code, or jurisdiction) includes or encompasses a specific law within it.
-
D.
haveLaw
Indicates that a governing body or jurisdiction possesses, enforces, or is characterized by a particular law or set of laws.
-
E.
containsLawOn
Indicates that one entity (such as a document, code, or regulation) includes or sets forth legal provisions concerning another entity or subject.
- 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_69e0b4f5b01081909452f654d2fc3f50 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c45e51f08190ac1ff59280ad741b |
completed | April 21, 2026, 12:27 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a593f481908beb457c29f1ce73 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53c4d6881909b4d0a716fa5ed4a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:44 p.m.