Triple
T9643854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juiz de Fora |
E233142
|
entity |
| Predicate | distanceToBeloHorizonte |
P89410
|
FINISHED |
| Object | about 260 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: about 260 km | Statement: [Juiz de Fora, distanceToBeloHorizonte, about 260 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBeloHorizonte Context triple: [Juiz de Fora, distanceToBeloHorizonte, about 260 km]
-
A.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
B.
distanceToRioDeJaneiroCity
Indicates the physical distance between a given entity’s location and the city of Rio de Janeiro.
-
C.
distanceFromFortaleza
Indicates the measured distance between a given entity or location and the city of Fortaleza.
-
D.
distanceToPantanal
Indicates the spatial distance between a given location and the Pantanal region.
-
E.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
- 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_69ca848a5a908190aad251f4137b0c3a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b7e2c488190b0f0dfa6d82618c8 |
completed | April 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b0263081908cf6df3eb07d71b0 |
completed | April 1, 2026, 8:22 a.m. |
| PDg | Predicate description generation | batch_69ccd9408c848190b84dd74d87f76273 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:12 p.m.