Triple
T18788484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Church of Colo |
E459442
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Colo |
—
|
NE NERFINISHED |
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: Colo | Statement: [Church of Colo, locatedIn, Colo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colo Context triple: [Church of Colo, locatedIn, Colo]
-
A.
Colo
chosen
Colo is a small city located in central Iowa, United States.
-
B.
Keystone, Colorado
Keystone, Colorado is a mountain community in Summit County known primarily as the base village and service hub for the nearby Keystone Resort ski area.
-
C.
Como, Colorado
Como, Colorado is a small historic unincorporated community and former railroad town located in the high plains of central Colorado.
-
D.
Columbia City
Columbia City is a historic, culturally diverse neighborhood in southeast Seattle known for its vibrant business district, arts scene, and early 20th-century architecture.
-
E.
Englewood
Englewood is a neighborhood on the South Side of Chicago, Illinois, historically known for its commercial hub and later for significant economic and social challenges.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e59783ea4c8190b1b04d08f65b7d19 |
completed | April 20, 2026, 3:03 a.m. |
Created at: April 10, 2026, 11:53 a.m.