Triple

T12696125
Position Surface form Disambiguated ID Type / Status
Subject Salang Tunnel E303337 entity
Predicate 1982FireCasualtiesEstimate P661 FINISHED
Object hundreds to possibly thousands of people 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: hundreds to possibly thousands of people | Statement: [Salang Tunnel, 1982FireCasualtiesEstimate, hundreds to possibly thousands of people]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: 1982FireCasualtiesEstimate
Context triple: [Salang Tunnel, 1982FireCasualtiesEstimate, hundreds to possibly thousands of people]
  • A. casualtiesEstimate chosen
    Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
  • B. militaryCasualtiesEstimate
    Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
  • C. secondaryCasualtiesFrom
    Indicates that an entity experiences indirect or collateral harm as a consequence of another primary event or source.
  • D. nativeCasualties
    Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
  • E. casualtiesImpact
    Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
  • 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_69d7bdef90d48190b46b88270e780946 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d962a32c6481908ddaddae4ea267bf completed April 10, 2026, 8:50 p.m.
PD Predicate disambiguation batch_69d960be63f081908a5ef5ef17a311bf completed April 10, 2026, 8:42 p.m.
Created at: April 9, 2026, 5:22 p.m.