Triple
T32820677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canela |
E839425
|
entity |
| Predicate | distanceFromPortoAlegre |
P205168
|
FINISHED |
| Object | approximately 120 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: approximately 120 km | Statement: [Canela, distanceFromPortoAlegre, approximately 120 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromPortoAlegre Context triple: [Canela, distanceFromPortoAlegre, approximately 120 km]
-
A.
distanceToSãoPaulo
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.
distanceToFlorianopolisApproxKm
Indicates an approximate distance, measured in kilometers, between a given entity and Florianópolis.
-
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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037cab06288190b093935f235ddff2 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:15 a.m.