Triple
T35421992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cookie Mountain |
E1023819
|
entity |
| Predicate | hasSubAreaOf |
P111186
|
FINISHED |
| Object | Twin Bridges region |
—
|
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: Twin Bridges region | Statement: [Cookie Mountain, hasSubAreaOf, Twin Bridges region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubAreaOf Context triple: [Cookie Mountain, hasSubAreaOf, Twin Bridges region]
-
A.
hasSubregionRelationWith
Indicates a relationship where one region is a subregion or contained part of another region.
-
B.
subArea
chosen
Indicates that one area is a constituent or contained part of a larger area.
-
C.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
D.
representsSubdivisionOf
Indicates that one administrative or territorial unit is a smaller, constituent part of a larger administrative or territorial unit.
-
E.
subdividedBy
Indicates that something is divided into smaller parts or sections by another entity or criterion.
- 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_69f76df6704081909900c60be10d5849 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffcc6182b48190afb598ced6500e66 |
completed | May 10, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69ffcbb363748190bc6f8d038fba44ff |
completed | May 10, 2026, 12:05 a.m. |
Created at: May 3, 2026, 4:03 p.m.