Triple
T15881870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ysleta |
E385091
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | El Paso |
E16599
|
NE 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: El Paso | Statement: [Ysleta, locatedIn, El Paso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: El Paso Context triple: [Ysleta, locatedIn, El Paso]
-
A.
El Paso
chosen
El Paso is a large border city in far western Texas known for its strong cultural ties with Mexico and its role as a major economic and transportation hub in the region.
-
B.
El Paso
El Paso is a municipality on the Spanish island of La Palma in the Canary Islands, known for its volcanic landscapes and proximity to the Cumbre Vieja ridge.
-
C.
Laredo
Laredo is a coastal town in northern Spain’s Cantabria region, known for its long sandy beaches and historic old quarter.
-
D.
Laredo
Laredo is a base-level trim of the Jeep Grand Cherokee SUV, offering essential features at a more affordable price point.
-
E.
Nogales
Nogales is a municipality and town in the state of Veracruz, Mexico, known for its mountainous terrain and role as part of the Orizaba metropolitan area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156173e248190901641d838ecb40f |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ec1091c8190a8e4c4db6180129a |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 4:51 a.m.