Triple
T33946989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claremont–Lebanon micropolitan area |
E870316
|
entity |
| Predicate | multiCounty |
P130848
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Claremont–Lebanon micropolitan area, multiCounty, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: multiCounty Context triple: [Claremont–Lebanon micropolitan area, multiCounty, true]
-
A.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
B.
includesCounty
Indicates that a larger geographic or administrative region contains or encompasses a specific county within its boundaries.
-
C.
interCountyLevel
chosen
Indicates that the relationship or action occurs between or spans across multiple counties or county-level jurisdictions.
-
D.
eachCountyHas
Indicates that for every county in a given set or context, there exists at least one associated item, attribute, or entity satisfying a specified condition.
-
E.
hasAdditionalCounty
Indicates that an entity is associated with one or more counties beyond its primary or originally specified 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_69f3499b0dd48190b07b4b60babcee02 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7064e906881909c3186c646145d34 |
completed | May 3, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69f70100ec1c8190a6b97f50e88891f2 |
completed | May 3, 2026, 8:02 a.m. |
Created at: May 1, 2026, 1:49 a.m.