Triple
T11702275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blumenau |
E278154
|
entity |
| Predicate | distanceToFlorianopolisApproxKm |
P101330
|
FINISHED |
| Object | 150 |
—
|
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: 150 | Statement: [Blumenau, distanceToFlorianopolisApproxKm, 150]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFlorianopolisApproxKm Context triple: [Blumenau, distanceToFlorianopolisApproxKm, 150]
-
A.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
B.
distanceToMaceio
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Maceió.
-
C.
distanceToBeloHorizonte
Indicates the spatial distance between an entity and the location of Belo Horizonte.
-
D.
distanceFromFortaleza
Indicates the measured distance between a given entity or location and the city of Fortaleza.
-
E.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a49b1080819096593733ee48a187 |
completed | April 10, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69d88a7b30948190b616a9db5c5488d5 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d89546a8688190b51455b5e12caf91 |
completed | April 10, 2026, 6:14 a.m. |
Created at: April 8, 2026, 9:40 p.m.