Triple

T5029887
Position Surface form Disambiguated ID Type / Status
Subject HART Communication Foundation E113269 entity
Predicate hasStandard P1371 FINISHED
Object HART E20658 NE 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: HART | Statement: [HART Communication Foundation, hasStandard, HART]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HART
Context triple: [HART Communication Foundation, hasStandard, HART]
  • A. HART
    HART is the public bus transportation system serving the Huntington area, providing local transit services to residents and visitors.
  • B. HART Communication Foundation
    The HART Communication Foundation is an industry consortium that develops and maintains the HART and WirelessHART communication standards for smart field instruments and process automation.
  • C. WirelessHART chosen
    WirelessHART is an industrial wireless communication standard designed for reliable, secure, and interoperable field device networking in process automation environments.
  • D. Hartis
    Hartis is a prominent Somali clan family that forms one of the major lineages within the broader Somali clan system.
  • E. Honeywell 316
    The Honeywell 316 is a 16-bit minicomputer introduced in the late 1960s, used widely for real-time control, industrial, and embedded applications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69bd443775e48190a646ffbfc4334723 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd739099a0819099c6201d4e1c5ee2 completed March 20, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea477efec8190a84a0186f5517a43 completed March 21, 2026, 2 p.m.
Created at: March 20, 2026, 1:36 p.m.