Triple
T29634758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teresópolis |
E755680
|
entity |
| Predicate | distanceFromRioDeJaneiroCity |
P83984
|
FINISHED |
| Object | about 90 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 90 km | Statement: [Teresópolis, distanceFromRioDeJaneiroCity, about 90 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromRioDeJaneiroCity Context triple: [Teresópolis, distanceFromRioDeJaneiroCity, about 90 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.
distanceFromAracaju
Indicates the measured distance between a given location and the city of Aracaju.
-
D.
distanceToBeloHorizonte
Indicates the spatial distance between an entity and the location of Belo Horizonte.
-
E.
distanceToSalvador_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Salvador.
- 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_69f0ef88fbe081908f0ad90c1c413f1c |
completed | April 28, 2026, 5:34 p.m. |
| NER | Named-entity recognition | batch_6a00a846a8f881908b073ad13a5af6ca |
completed | May 10, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_6a00a7aae194819085e1a2fd406f7922 |
completed | May 10, 2026, 3:43 p.m. |
Created at: April 28, 2026, 6:43 p.m.