Triple
T32477105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noida Sector 18 |
E830004
|
entity |
| Predicate | hasNearbySector |
P141258
|
FINISHED |
| Object | Noida Sector 17 |
—
|
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: Noida Sector 17 | Statement: [Noida Sector 18, hasNearbySector, Noida Sector 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbySector Context triple: [Noida Sector 18, hasNearbySector, Noida Sector 17]
-
A.
hasNearbyGeographicalArea
chosen
Indicates that one geographical area is located in close spatial proximity to another geographical area.
-
B.
hasNearbySquare
Indicates that one entity has at least one square-shaped entity located close to it in space.
-
C.
hasNearbyPrecinct
Indicates that one location has a police precinct or similar administrative station situated close to it in geographic proximity.
-
D.
hasNearbyEntityType
Indicates that an entity has at least one other entity of a specified type located within a defined nearby spatial or contextual range.
-
E.
hasNearbyBoundary
Indicates that one entity’s boundary lies close to, but does not necessarily touch or coincide with, the boundary of 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_69f3491ff3b48190b50a7fa00bb05b1f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff49f888348190b9c55afa73b99e6a |
completed | May 9, 2026, 2:51 p.m. |
| PD | Predicate disambiguation | batch_69ff49614ef88190ac70b034c55ad738 |
completed | May 9, 2026, 2:49 p.m. |
Created at: May 1, 2026, 12:58 a.m.