Triple
T35961814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luján, Buenos Aires |
E1040017
|
entity |
| Predicate | distanceToBuenosAiresCity |
P23952
|
FINISHED |
| Object | approximately 68 km west |
—
|
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 68 km west | Statement: [Luján, Buenos Aires, distanceToBuenosAiresCity, approximately 68 km west]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBuenosAiresCity Context triple: [Luján, Buenos Aires, distanceToBuenosAiresCity, approximately 68 km west]
-
A.
distanceFromBuenosAires
chosen
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
B.
distanceToBahíaBlanca
Indicates the measured distance between a given entity and the location of Bahía Blanca.
-
C.
distanceToPuntaDelEste
Indicates the measured distance between a given entity’s location and the location of Punta del Este.
-
D.
distanceToNeuquénCity
Indicates the spatial distance between a given location and the city of Neuquén.
-
E.
distanceToNeuquénCity_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Neuquén.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:07 p.m.