Triple
T20632970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tepehuán culture |
E507003
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Durango |
—
|
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: Durango | Statement: [Tepehuán culture, region, Durango]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Durango Context triple: [Tepehuán culture, region, Durango]
-
A.
Durango
chosen
Durango is a state in north-central Mexico known for its rugged mountainous terrain, significant mining history, and role as a setting for classic Western films.
-
B.
Durango
Durango is a historic mountain town in southwestern Colorado known for its scenic landscapes, outdoor recreation, and the Durango & Silverton Narrow Gauge Railroad.
-
C.
Durango
Durango is a historic town in the Basque Country of northern Spain, known for its medieval center and cultural heritage.
-
D.
Taos
Taos is a historic town in northern New Mexico known for its rich Native American and Spanish heritage, thriving arts community, and proximity to scenic high-desert and mountain landscapes.
-
E.
Pagosa Springs
Pagosa Springs is a small Colorado town renowned for its natural hot springs and scenic setting in the San Juan Mountains.
- 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_69e0b4bd4a0081908d4e97a590a33fb2 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad0bdcd88190a59d68e03370b271 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.