Triple
T14997484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Cienega Mud Springs |
E373994
|
entity |
| Predicate | hasCountyFIPS |
P227
|
FINISHED |
| Object | 037 (Los Angeles County) |
—
|
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: 037 (Los Angeles County) | Statement: [La Cienega Mud Springs, hasCountyFIPS, 037 (Los Angeles County)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountyFIPS Context triple: [La Cienega Mud Springs, hasCountyFIPS, 037 (Los Angeles County)]
-
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.
hasParentCounty
Indicates that a given county is administratively or geographically contained within, and subordinate to, a specified parent county.
-
D.
hasCountyEquivalentStatus
Indicates that an entity holds an administrative or legal status equivalent to that of a county within a given jurisdiction.
-
E.
hasCentralCounty
Indicates that an administrative or geographic region is associated with a specific county that serves as its central or primary county.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded718e4288190b5e144f82299a194 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.