Triple
T29907322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Humaitá, Rio de Janeiro, Brazil |
E759573
|
entity |
| Predicate | closestMetroStation |
P26735
|
FINISHED |
| Object | Botafogo Station |
—
|
NE NERFINISHED |
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: Botafogo Station | Statement: [Humaitá, Rio de Janeiro, Brazil, closestMetroStation, Botafogo Station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: closestMetroStation Context triple: [Humaitá, Rio de Janeiro, Brazil, closestMetroStation, Botafogo Station]
-
A.
nearMetroStation
Indicates that one entity is located close to or within a short walking distance of a metro (subway) station.
-
B.
nearestSuburbanRailwayStation
Indicates the relationship where a given place is associated with the suburban railway station that is geographically closest to it.
-
C.
nearestEntranceStation
Indicates that one station is the closest entrance station to a given location or entity compared to all other candidate stations.
-
D.
nearestUndergroundStation
chosen
Indicates the relationship where a specific underground (subway/metro) station is the closest one in distance to a given location or entity.
-
E.
nearestMajorMetro
Indicates the relationship where a given location is associated with the closest large metropolitan area to it.
- 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_69f224600590819085e148a01c056ef6 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
Created at: April 29, 2026, 6:09 p.m.