Triple
T34775012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paranapiacaba region |
E1002477
|
entity |
| Predicate | distanceFromSãoPauloApproxKm |
P43610
|
FINISHED |
| Object | about 50 |
—
|
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 50 | Statement: [Paranapiacaba region, distanceFromSãoPauloApproxKm, about 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSãoPauloApproxKm Context triple: [Paranapiacaba region, distanceFromSãoPauloApproxKm, about 50]
-
A.
distanceToSãoPaulo
chosen
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
B.
distanceToCuritiba
Indicates the physical distance between a given entity’s location and the city of Curitiba.
-
C.
distanceToBeloHorizonte
Indicates the spatial distance between an entity and the location of Belo Horizonte.
-
D.
distanceFromAracaju
Indicates the measured distance between a given location and the city of Aracaju.
-
E.
distanceToFlorianopolisApproxKm
Indicates an approximate distance, measured in kilometers, between a given entity and Florianópolis.
- 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_69f76db30a108190bb57ca95b873e5bb |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fd7aac8190873077e63873aa72 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 3:59 p.m.