Triple

T21786563
Position Surface form Disambiguated ID Type / Status
Subject ArcelorMittal Zenica steelworks E537851 entity
Predicate hasSafetyRisk P103131 FINISHED
Object heavy industry occupational hazards 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: heavy industry occupational hazards | Statement: [ArcelorMittal Zenica steelworks, hasSafetyRisk, heavy industry occupational hazards]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasSafetyRisk
Context triple: [ArcelorMittal Zenica steelworks, hasSafetyRisk, heavy industry occupational hazards]
  • A. hasRiskFrom
    Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
  • B. safetyImplication chosen
    Indicates that one entity has a consequence, effect, or relevance for the safety or risk level associated with another entity or situation.
  • C. isHazardTo
    Indicates that one entity poses a potential source of danger, harm, or risk to another entity.
  • D. hasNotableHazard
    Indicates that an entity is associated with a significant risk, danger, or harmful condition that is noteworthy or exceptional.
  • E. safetyRationale
    Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
  • 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_69e0c47198f881908cb0d237266c10e9 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0621b5d6881908b74bc999c94fa3d completed April 28, 2026, 7:30 a.m.
PD Predicate disambiguation batch_69e6be6299988190a34c98fa76d94700 completed April 21, 2026, 12:01 a.m.
Created at: April 16, 2026, 6:52 p.m.