Triple
T27282696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yemenite War of 1979 |
E688380
|
entity |
| Predicate | hadUnderlyingCause |
P12801
|
FINISHED |
| Object | disputes over the North–South Yemen border |
—
|
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: disputes over the North–South Yemen border | Statement: [Yemenite War of 1979, hadUnderlyingCause, disputes over the North–South Yemen border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadUnderlyingCause Context triple: [Yemenite War of 1979, hadUnderlyingCause, disputes over the North–South Yemen border]
-
A.
hadCondition
Indicates that an entity experienced or was diagnosed with a particular medical or health-related condition.
-
B.
underlyingIssue
chosen
Indicates that one situation, problem, or condition is the fundamental cause or root problem behind another.
-
C.
hasEtiology
Indicates that one entity is the cause, origin, or underlying reason for the occurrence or existence of another entity or condition.
-
D.
eligibleCause
Indicates that one entity qualifies as a valid or acceptable cause or reason for another entity or outcome.
-
E.
causeStatus
Indicates that one entity brings about, initiates, or is responsible for a particular state or condition in 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_69ef355998e08190bdff849e8f33adce |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f6275281288190baf993960cf87fac |
completed | May 2, 2026, 4:33 p.m. |
| PD | Predicate disambiguation | batch_69f623a91b9c8190b2e2fdbc55cb89b6 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 11:09 a.m.