Triple
T37290796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mirs of Hunza |
E925659
|
entity |
| Predicate | borderRelations |
P137589
|
FINISHED |
| Object | Qing China |
—
|
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: Qing China | Statement: [Mirs of Hunza, borderRelations, Qing China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderRelations Context triple: [Mirs of Hunza, borderRelations, Qing China]
-
A.
countryBorderRelation
chosen
Indicates that two countries share a common land or maritime boundary with each other.
-
B.
diplomaticRelation
Indicates a formal relationship of negotiation, communication, or representation between political entities, typically states or governments.
-
C.
politicalRelation
Indicates a relationship between entities that involves political alignment, influence, affiliation, conflict, or cooperation within a political context.
-
D.
borderingRepublic
Indicates that one republic shares a land or maritime border with another republic.
-
E.
establishesBilateralRelationsBetween
Indicates the action of formally creating mutual diplomatic or cooperative relations between two entities.
- 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_69f76eb0f86c819098dee07393e69ec3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a00053d8004819097ad9cf6431a20a3 |
completed | May 10, 2026, 4:10 a.m. |
| PD | Predicate disambiguation | batch_6a0004b3a82c81908e2bf9a533a93eb6 |
completed | May 10, 2026, 4:08 a.m. |
Created at: May 3, 2026, 4:16 p.m.