Triple

T11002979
Position Surface form Disambiguated ID Type / Status
Subject Markov random field E260046 entity
Predicate hasAlternativeName P39 FINISHED
Object MRF E123458 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: MRF | Statement: [Markov random field, hasAlternativeName, MRF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MRF
Context triple: [Markov random field, hasAlternativeName, MRF]
  • A. MRF chosen
    MRF (Media Resource Function) is a core network component in IP Multimedia Subsystem (IMS) architectures responsible for handling media processing tasks such as mixing, transcoding, and media stream manipulation for real-time communication services.
  • B. MRAF
    MRAF is the highest rank in the Royal Air Force, equivalent to a five-star air officer and typically held only in wartime or as an honorary appointment.
  • C. M&R
    M&R is the commonly used abbreviation for Murray & Roberts, a South African engineering and construction services company.
  • D. MUF
    MUF is the youth wing of Sweden's Moderate Party, engaging young people in center-right politics and policy issues.
  • E. MRS
    MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797546f448190946ee6442d657dc5 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.