Triple
T6969106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Film Nagar, Hyderabad |
E161557
|
entity |
| Predicate | nearbyCityFeature |
P61362
|
FINISHED |
| Object | close to central Hyderabad |
—
|
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: close to central Hyderabad | Statement: [Film Nagar, Hyderabad, nearbyCityFeature, close to central Hyderabad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyCityFeature Context triple: [Film Nagar, Hyderabad, nearbyCityFeature, close to central Hyderabad]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
nearbyFeature
Indicates that one entity is located close to or in the immediate vicinity of another entity.
-
C.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
D.
regionCapitalNearby
Indicates that a capital city of a region is located close to the referenced place or entity.
-
E.
nearbyTo
chosen
Indicates that one entity is located close in distance or position to another entity.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db152b2081909271493a5d1469fb |
completed | March 27, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.