Triple
T30409689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Curicica |
E773579
|
entity |
| Predicate | hasNearbyMajorArea |
P139465
|
FINISHED |
| Object | Barra da Tijuca |
—
|
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: Barra da Tijuca | Statement: [Curicica, hasNearbyMajorArea, Barra da Tijuca]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyMajorArea Context triple: [Curicica, hasNearbyMajorArea, Barra da Tijuca]
-
A.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
B.
hasNearbyGeographicalArea
Indicates that one geographical area is located in close spatial proximity to another geographical area.
-
C.
hasNearbyMajorCityCountry
Indicates that an entity has a nearby major city located in the specified country.
-
D.
nearestMajorArea
chosen
Indicates the relationship where a given location is associated with the closest significant geographic or administrative area.
-
E.
hasMajorCompanyNearby
Indicates that a location or entity is situated close to at least one large or significant company.
- 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_69f22490b8b48190ab10c886a8d58c89 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fea2d0a4d08190aa06aeb902a02d5a |
completed | May 9, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69fea24698348190b9b992a8e7cdbcd0 |
completed | May 9, 2026, 2:56 a.m. |
Created at: April 29, 2026, 8:04 p.m.