Triple
T35732140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villa Cura Brochero |
E1032784
|
entity |
| Predicate | distanceToCórdobaCity_km |
P105214
|
FINISHED |
| Object | approximately 160 |
—
|
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: approximately 160 | Statement: [Villa Cura Brochero, distanceToCórdobaCity_km, approximately 160]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCórdobaCity_km Context triple: [Villa Cura Brochero, distanceToCórdobaCity_km, approximately 160]
-
A.
distanceFromCórdobaCity
chosen
Indicates the spatial distance between an entity and the city of Córdoba.
-
B.
distanceFromSeville
Indicates the spatial distance separating an entity from the location of Seville.
-
C.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
-
D.
distanceFromSaltaByRoad_km
Indicates the distance in kilometers between an entity and Salta when traveling by road.
-
E.
distanceToLucenaCity_km
Indicates the distance, measured in kilometers, between a given entity’s location and Lucena City.
- 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:05 p.m.