Triple
T20506539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sheriff court |
E503447
|
entity |
| Predicate | numberOfSheriffdoms |
P47986
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [sheriff court, numberOfSheriffdoms, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSheriffdoms Context triple: [sheriff court, numberOfSheriffdoms, 6]
-
A.
hasCountySheriff
Indicates that a jurisdiction or area is served or overseen by a specific county sheriff.
-
B.
numberOfJurisdictions
chosen
Indicates the count of distinct legal or administrative jurisdictions associated with or applicable to an entity or situation.
-
C.
hasPoliceDepartment
Indicates that an entity possesses, is served by, or is administratively associated with a police department.
-
D.
enforcementAgency
Indicates that one entity serves as the authority responsible for enforcing laws, rules, or regulations related to another entity.
-
E.
numberOfRegencies
Indicates the count of distinct regencies associated with or applicable to a given entity.
- 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_69e0b4b1e52c8190894281cf7e3283ab |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69dc82dd0819085d64d65a1e72c70 |
completed | April 20, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69e59fcdf6e08190a604204615dc56e6 |
completed | April 20, 2026, 3:38 a.m. |
Created at: April 16, 2026, 11:36 a.m.