Triple

T10611571
Position Surface form Disambiguated ID Type / Status
Subject Ava E276019 entity
Predicate damageFromEarthquake P54661 FINISHED
Object severe destruction of palaces and monasteries 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: severe destruction of palaces and monasteries | Statement: [Ava, damageFromEarthquake, severe destruction of palaces and monasteries]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: damageFromEarthquake
Context triple: [Ava, damageFromEarthquake, severe destruction of palaces and monasteries]
  • A. facedMajorEarthquake
    Indicates that an entity has experienced or been subjected to a significant or severe earthquake event.
  • B. populationImpact2010Earthquake
    Indicates the effect or consequences that the 2010 earthquake had on a population, such as changes in size, distribution, or demographic characteristics.
  • C. earthquakeType
    Indicates the specific classification or category of an earthquake based on its characteristics or cause.
  • D. locationDuringEarthquake
    Indicates the place where an entity is situated at the time an earthquake occurs.
  • E. infrastructureDamage chosen
    Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df5a1450819082ad445712fb7868 completed April 8, 2026, 11:06 p.m.
PD Predicate disambiguation batch_69d6dd7a223c8190854409d76368f3e8 completed April 8, 2026, 10:58 p.m.
Created at: April 8, 2026, 7:33 p.m.