Triple

T1924481
Position Surface form Disambiguated ID Type / Status
Subject Medical Research Council E40797 entity
Predicate shortName P43 FINISHED
Object MRC
MRC is a major UK organization that funds and supports medical research to improve human health.
 E214988 NE FINISHED

How this triple was built (4 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: MRC | Statement: [Medical Research Council, shortName, MRC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MRC
Context triple: [Medical Research Council, shortName, MRC]
  • A. MRC
    MRC is an American independent film and television studio known for producing and financing a wide range of acclaimed movies and TV series.
  • B. MCRC
    MCRC is the United States Marine Corps Recruiting Command responsible for enlisting and accessing new Marines into the Corps.
  • C. MCS
    MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
  • D. MRS
    MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
  • E. MRF
    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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MRC
Triple: [Medical Research Council, shortName, MRC]
Generated description
MRC is a major UK organization that funds and supports medical research to improve human health.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MRC
Target entity description: MRC is a major UK organization that funds and supports medical research to improve human health.
  • A. MRC
    MRC is an American independent film and television studio known for producing and financing a wide range of acclaimed movies and TV series.
  • B. MCRC
    MCRC is the United States Marine Corps Recruiting Command responsible for enlisting and accessing new Marines into the Corps.
  • C. MCS
    MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
  • D. MRS
    MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
  • E. MRF
    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.
  • F. None of above. chosen

Provenance (5 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_69a8864711648190b07bed24ed76258e completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb2359ca0819082b514a34c469b21 completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3e6678881908d72de7e0f19a648 completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf472aca881908d99cf5bfcae3094 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf50d93c88190aa1cbf96526558b6 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:35 p.m.