Triple
T36843477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rawatbhata |
E910473
|
entity |
| Predicate | hasNearbyResidentialColony |
P148757
|
FINISHED |
| Object | Anu Nagar (RAPS township) |
—
|
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: Anu Nagar (RAPS township) | Statement: [Rawatbhata, hasNearbyResidentialColony, Anu Nagar (RAPS township)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyResidentialColony Context triple: [Rawatbhata, hasNearbyResidentialColony, Anu Nagar (RAPS township)]
-
A.
hasNearbyColony
chosen
Indicates that one entity has another entity located close enough to be considered a nearby colony.
-
B.
hasNearbyCivicBuilding
Indicates that one entity is located close to, or in the immediate vicinity of, a civic building such as a government, public service, or community facility.
-
C.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
-
D.
residesNear
Indicates that one entity lives or is located in close physical proximity to another entity.
-
E.
hasNearbySettlementDensity
Indicates that an entity is associated with a concentration of settlements located within a nearby surrounding area.
- 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_69f76e7f65a881908651b702da592b6d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a00708ab11081909641d245c4282aa0 |
completed | May 10, 2026, 11:48 a.m. |
| PD | Predicate disambiguation | batch_6a00703daa0081908903ce3a28084d35 |
completed | May 10, 2026, 11:47 a.m. |
Created at: May 3, 2026, 4:13 p.m.