Triple
T19311847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laos and Vietnam |
E482990
|
entity |
| Predicate | haveBilateralRelations |
P40062
|
FINISHED |
| Object | diplomatic |
—
|
LITERAL FINISHED |
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: diplomatic | Statement: [Laos and Vietnam, haveBilateralRelations, diplomatic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveBilateralRelations Context triple: [Laos and Vietnam, haveBilateralRelations, diplomatic]
-
A.
bilateralRelation
chosen
Indicates a mutual or two-way relationship between two entities, where each affects or interacts with the other.
-
B.
diplomaticRelation
Indicates a formal relationship of negotiation, communication, or representation between political entities, typically states or governments.
-
C.
diplomaticRelationBetween
Indicates the existence of a formal diplomatic relationship or ties between two political entities or states.
-
D.
politicalRelation
Indicates a relationship between entities that involves political alignment, influence, affiliation, conflict, or cooperation within a political context.
-
E.
hasMilitaryCooperationAt
Indicates that two or more entities engage in military cooperation at or in relation to a specific location or venue.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604ce5de081909811c49f56ba94bb |
completed | April 20, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69e4dd0ef66881909d489d634eee817a |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:32 p.m.