Triple
T27845275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lules |
E703804
|
entity |
| Predicate | distanceToSanMiguelDeTucumán |
P200592
|
FINISHED |
| Object | approximately 20 km |
—
|
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 20 km | Statement: [Lules, distanceToSanMiguelDeTucumán, approximately 20 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSanMiguelDeTucumán Context triple: [Lules, distanceToSanMiguelDeTucumán, approximately 20 km]
-
A.
distanceToBahíaBlanca
Indicates the measured distance between a given entity and the location of Bahía Blanca.
-
B.
distanceToNeuquénCity_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Neuquén.
-
C.
distanceToNeuquénCity
Indicates the spatial distance between a given location and the city of Neuquén.
-
D.
distanceFromCafayateByRoad_km
Indicates the distance in kilometers from Cafayate to another location when traveling by road.
-
E.
distanceFromSaltaByRoad_km
Indicates the distance in kilometers between an entity and Salta when traveling by road.
- F. None of above. chosen
Provenance (4 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_69ef840d9e3c819093615ebff4ec22be |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69ff9a25407c81909faa86e72a7a9d17 |
completed | May 9, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69ff99c613688190a03b2f93d5ccad2b |
completed | May 9, 2026, 8:32 p.m. |
| PDg | Predicate description generation | batch_69ff9a24996c8190ba2c5345d317cbdb |
completed | May 9, 2026, 8:33 p.m. |
Created at: April 27, 2026, 6:06 p.m.