Triple
T9643853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juiz de Fora |
E233142
|
entity |
| Predicate | distanceToRioDeJaneiro |
P83984
|
FINISHED |
| Object | about 180 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 180 km | Statement: [Juiz de Fora, distanceToRioDeJaneiro, about 180 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRioDeJaneiro Context triple: [Juiz de Fora, distanceToRioDeJaneiro, about 180 km]
-
A.
distanceToRioDeJaneiroCity
chosen
Indicates the physical distance between a given entity’s location and the city of Rio de Janeiro.
-
B.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
C.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
-
D.
distanceFromFortaleza
Indicates the measured distance between a given entity or location and the city of Fortaleza.
-
E.
distanceFromLisbon
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
- 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_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. |
Created at: March 30, 2026, 8:12 p.m.