Triple
T37124035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azogues |
E919340
|
entity |
| Predicate | distanceToCuenca |
P92955
|
FINISHED |
| Object | about 34 kilometres |
—
|
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: about 34 kilometres | Statement: [Azogues, distanceToCuenca, about 34 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCuenca Context triple: [Azogues, distanceToCuenca, about 34 kilometres]
-
A.
distanceFromCuenca
chosen
Indicates the measured spatial distance between a given entity or location and the city of Cuenca.
-
B.
distanceToGuayaquil_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Guayaquil.
-
C.
distanceFromQuito
Indicates the spatial distance between a given entity or location and the city of Quito.
-
D.
distanceFromCafayateByRoad_km
Indicates the distance in kilometers from Cafayate to another location when traveling by road.
-
E.
distanceFromCusco
Indicates the measured spatial distance between a given location or entity and the city of Cusco.
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.