Triple
T24827793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concepción del Uruguay |
E621242
|
entity |
| Predicate | distanceToBuenosAiresApproxKm |
P23952
|
FINISHED |
| Object | 300 |
—
|
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: 300 | Statement: [Concepción del Uruguay, distanceToBuenosAiresApproxKm, 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBuenosAiresApproxKm Context triple: [Concepción del Uruguay, distanceToBuenosAiresApproxKm, 300]
-
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.
distanceFromUshuaia_km
Indicates the distance, measured in kilometers, between a given entity’s location and Ushuaia.
-
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_69e2fac0c3b881909110e5a56c6fa46f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f61f12b0f08190bc4a16907941864c |
completed | May 2, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 18, 2026, 5:06 a.m.