Triple
T35603271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barmer railway station |
E1028811
|
entity |
| Predicate | hasNearbyBorderRoute |
P195768
|
FINISHED |
| Object | India–Pakistan border (via Munabao) |
—
|
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: India–Pakistan border (via Munabao) | Statement: [Barmer railway station, hasNearbyBorderRoute, India–Pakistan border (via Munabao)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyBorderRoute Context triple: [Barmer railway station, hasNearbyBorderRoute, India–Pakistan border (via Munabao)]
-
A.
hasNearbyBoundary
Indicates that one entity’s boundary lies close to, but does not necessarily touch or coincide with, the boundary of another entity.
-
B.
hasNearbyBoroughBorder
Indicates that the border of one borough is geographically close to the border of another borough.
-
C.
hasNearbyCrossingPoint
Indicates that one location has a crossing point (such as a bridge, crosswalk, or intersection) situated close to it.
-
D.
hasNearbyInternationalRoad
chosen
Indicates that an entity is located close to an international road or highway that crosses national borders.
-
E.
hasNearbyCountyBorder
Indicates that the borders of two counties are geographically close to each other, though not necessarily directly adjacent.
- 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_69f76e0653ec81909b1b813c126c6574 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a002249ee388190a9501ee7630dc658 |
completed | May 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_6a002189273881909b6b687e2d61f5b1 |
completed | May 10, 2026, 6:11 a.m. |
Created at: May 3, 2026, 4:05 p.m.