Triple
T964691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washoe County |
E20811
|
entity |
| Predicate | hasMetropolitanStatus |
P12101
|
FINISHED |
| Object | metropolitan 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: metropolitan county | Statement: [Washoe County, hasMetropolitanStatus, metropolitan county]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetropolitanStatus Context triple: [Washoe County, hasMetropolitanStatus, metropolitan county]
-
A.
hasMetropolitan
Indicates that an entity is associated with, served by, or located within a specific metropolitan area.
-
B.
hasMetropolitanCounty
chosen
Indicates that an entity is associated with, located within, or administered by a specific metropolitan county.
-
C.
isMetropolitanCoreOf
Indicates that one area functions as the central, most urbanized core within a larger metropolitan region or system.
-
D.
isInMetropolitanAreaRank
Indicates that one metropolitan area holds a specific rank or position relative to others based on a defined metropolitan-area-related criterion (such as size, population, or importance).
-
E.
hasMetropolitanAreaName
Indicates that an entity is associated with a metropolitan area identified by a specific name.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4303e5881909d101d11f9732c75 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a42c1481908d940cbe0aefdd3b |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.