Triple
T18131987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morón |
E434034
|
entity |
| Predicate | distanceToBuenosAiresCityCenter_km |
P23952
|
FINISHED |
| Object | approximately 20 |
—
|
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 | Statement: [Morón, distanceToBuenosAiresCityCenter_km, approximately 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBuenosAiresCityCenter_km Context triple: [Morón, distanceToBuenosAiresCityCenter_km, approximately 20]
-
A.
distanceFromBuenosAires
chosen
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
B.
distanceToPuntaDelEste
Indicates the measured distance between a given entity’s location and the location of Punta del Este.
-
C.
distanceFromBariloche
Indicates the measured distance between a given entity or location and Bariloche.
-
D.
distanceFromSaltaByRoad_km
Indicates the distance in kilometers between an entity and Salta when traveling by road.
-
E.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
- 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_69d8b909e8cc81908df4cc2b8ea6d11f |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddf2c68881909dfbe59df15ddccc |
completed | April 19, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_69e43317d11c81908d1dc14921566b47 |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:29 a.m.