Triple
T30825595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenner |
E785052
|
entity |
| Predicate | regionCode_FIPSCounty |
P227
|
FINISHED |
| Object | 06097 |
—
|
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: 06097 | Statement: [Jenner, regionCode_FIPSCounty, 06097]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionCode_FIPSCounty Context triple: [Jenner, regionCode_FIPSCounty, 06097]
-
A.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
B.
FIPSCode
chosen
Indicates the standardized Federal Information Processing Standards (FIPS) code assigned to identify a specific geographic or administrative entity.
-
C.
hasCountyCodeType
Indicates that an entity is associated with a specific type or classification of county code.
-
D.
USStateFIPSLocation
Indicates a locational relationship where a place or entity is situated within, or associated with, a U.S. state identified by its FIPS (Federal Information Processing Standards) code.
-
E.
districtCode
Indicates that an entity is associated with, or identified by, a specific administrative district code.
- 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_69f224b6642481909e8d701de2cd1a53 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe610e1f6881908f10070ba64643cf |
completed | May 8, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69fe604c6c008190ad659e9b9fa82f7b |
completed | May 8, 2026, 10:14 p.m. |
Created at: April 29, 2026, 8:44 p.m.