Triple
T33561365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campestre Churubusco |
E859630
|
entity |
| Predicate | nearCulturalAttraction |
P145776
|
FINISHED |
| Object | Coyoacán museums |
—
|
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: Coyoacán museums | Statement: [Campestre Churubusco, nearCulturalAttraction, Coyoacán museums]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearCulturalAttraction Context triple: [Campestre Churubusco, nearCulturalAttraction, Coyoacán museums]
-
A.
nearCulturalInstitution
chosen
Indicates that one entity is located close to, or in the immediate vicinity of, a cultural institution such as a museum, theater, gallery, or similar venue.
-
B.
nearbyRoyalSite
Indicates that one place or object is located close to a site associated with royalty, such as a palace, castle, or royal residence.
-
C.
nearestTouristDestination
Indicates that one location is the closest tourist destination to another specified location.
-
D.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69f3497c1d288190a844ea699914e038 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f7127d848190916a5a45fe3b6578 |
completed | May 3, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:40 a.m.