Triple
T8668643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guapimirim |
E205739
|
entity |
| Predicate | distanceToRioDeJaneiroCity |
P83984
|
FINISHED |
| Object | approximately 70 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 70 km | Statement: [Guapimirim, distanceToRioDeJaneiroCity, approximately 70 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRioDeJaneiroCity Context triple: [Guapimirim, distanceToRioDeJaneiroCity, approximately 70 km]
-
A.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
B.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
-
C.
distanceFromFortaleza
Indicates the measured distance between a given entity or location and the city of Fortaleza.
-
D.
distanceFromLisbon
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
-
E.
distanceToShanghai
Indicates the measured or specified distance between a given entity’s location and the city of Shanghai.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a48b548190b78259072b1224ee |
completed | March 31, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc46c330bc8190a9b644078881c6ff |
completed | March 31, 2026, 10:12 p.m. |
Created at: March 30, 2026, 6:31 p.m.