Triple

T12129923
Position Surface form Disambiguated ID Type / Status
Subject Are Ona Kakanfo E288906 entity
Predicate riskAssociatedWithOffice P103548 FINISHED
Object high mortality in battle 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: high mortality in battle | Statement: [Are Ona Kakanfo, riskAssociatedWithOffice, high mortality in battle]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: riskAssociatedWithOffice
Context triple: [Are Ona Kakanfo, riskAssociatedWithOffice, high mortality in battle]
  • A. riskAddressed
    Indicates that a particular risk has been identified and is being mitigated, managed, or otherwise handled by an associated action, control, or measure.
  • B. hasAssociatedOffice
    Indicates that an entity is linked to or connected with a particular office in an official or functional capacity.
  • C. executiveOfficeInvolved
    Indicates that an executive-level office is directly involved in, overseeing, or responsible for the referenced action or relationship.
  • D. associatedWithGovernmentOfficial
    Indicates a relationship in which an entity has a connection, involvement, or affiliation with a government official.
  • E. limitsOffice
    Indicates that one entity imposes a restriction or cap on the scope, duration, or powers of another entity’s office or official position.
  • F. None of above. chosen

Provenance (4 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_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d91841615c819097f20a7447a1b8f4 completed April 10, 2026, 3:33 p.m.
PD Predicate disambiguation batch_69d91508f8008190b3a90ec0bf0953ca completed April 10, 2026, 3:19 p.m.
PDg Predicate description generation batch_69d9183ec1008190b437b7d5e1f52830 completed April 10, 2026, 3:33 p.m.
Created at: April 8, 2026, 9:49 p.m.