Triple
T35136650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rupaidiha |
E1014590
|
entity |
| Predicate | neighbouringCountryTown |
P78863
|
FINISHED |
| Object | Nepalgunj |
—
|
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: Nepalgunj | Statement: [Rupaidiha, neighbouringCountryTown, Nepalgunj]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighbouringCountryTown Context triple: [Rupaidiha, neighbouringCountryTown, Nepalgunj]
-
A.
neighbouringMunicipality
Indicates that one municipality directly borders and is adjacent to another municipality.
-
B.
isNeighboringCityOf
Indicates that one city is geographically adjacent to or directly borders another city.
-
C.
oppositeTownAcrossBorder
chosen
Indicates that one town is located directly across a border from another town, positioned as its opposite counterpart.
-
D.
oppositeTownCountry
Indicates that two locations are situated in opposing or contrasting town and country settings, such that one is urban while the other is rural.
-
E.
twinTownInCountry
Indicates that a town’s twin or sister city relationship is specifically with a town located in the given country.
- 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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ca1c3dc8190852c462de4f47bee |
completed | May 3, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69f78b9106008190930b3b3675b737d6 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:02 p.m.