Triple

T22628131
Position Surface form Disambiguated ID Type / Status
Subject Kalashnikov rifle family E558478 entity
Predicate hasPart P35 FINISHED
Object AKM NE NERFINISHED

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: AKM | Statement: [Kalashnikov rifle family, hasPart, AKM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AKM
Context triple: [Kalashnikov rifle family, hasPart, AKM]
  • A. AKM chosen
    The AKM is a modernized, widely produced variant of the AK-47 assault rifle, known for its reliability, simplicity, and extensive use in militaries and conflicts around the world.
  • B. AKOM
    AKOM is a South Korean animation studio known for providing outsourced animation work on numerous popular Western television series, including many episodes of the original Transformers cartoon.
  • C. AMKC
    AMKC is a large jail facility on Rikers Island in New York City, named after former Correction Commissioner Anna M. Kross.
  • D. AKRM
    AKRM is a Mauritian institution dedicated to the promotion, standardization, and development of the Mauritian Creole language.
  • E. AKJ
    AKJ is the ICAO airline designator assigned to Akasa Air, a low-cost carrier based in India.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245467d9881908d6985bd0db7a1f1 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f16e3ed1d48190a093013d829901b9 completed April 29, 2026, 2:34 a.m.
Created at: April 17, 2026, 3:02 p.m.