Triple
T4882670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabarmati (locality in Ahmedabad) |
E109365
|
entity |
| Predicate | hasNearbyHealthcareFacilities |
P5648
|
FINISHED |
| Object | hospitals and clinics in northern Ahmedabad |
—
|
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: hospitals and clinics in northern Ahmedabad | Statement: [Sabarmati (locality in Ahmedabad), hasNearbyHealthcareFacilities, hospitals and clinics in northern Ahmedabad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyHealthcareFacilities Context triple: [Sabarmati (locality in Ahmedabad), hasNearbyHealthcareFacilities, hospitals and clinics in northern Ahmedabad]
-
A.
hasNearbyFacility
chosen
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
B.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
C.
hasNearbyInstitution
Indicates that one entity is located close to or in the immediate vicinity of an institution.
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
E.
containsMedicalDistrict
Indicates that one administrative or geographic area includes a designated medical district within its boundaries.
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddfff0c81908fb148a6f6508334 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2be5e881909f6ec9c3bcde49f3 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:27 p.m.