Triple

T14395107
Position Surface form Disambiguated ID Type / Status
Subject dotCover E356931 entity
Predicate supportsTestFramework P58147 FINISHED
Object MSpec
MSpec (Machine.Specifications) is a behavior-driven development testing framework for .NET that focuses on writing human-readable, specification-style tests.
E1096574 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: MSpec | Statement: [dotCover, supportsTestFramework, MSpec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MSpec
Context triple: [dotCover, supportsTestFramework, MSpec]
  • A. MSO
    MSO is the three-letter FAA airport code for Missoula Montana Airport, a regional air transportation hub serving Missoula and western Montana.
  • B. MSO
    MSO is the National Rail station code for Moston railway station in Greater Manchester, England.
  • C. MSA
    MSA is a common abbreviation for a metropolitan statistical area, a region defined by the U.S. Office of Management and Budget for statistical and demographic analysis.
  • D. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • E. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • 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: MSpec
Triple: [dotCover, supportsTestFramework, MSpec]
Generated description
MSpec (Machine.Specifications) is a behavior-driven development testing framework for .NET that focuses on writing human-readable, specification-style tests.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MSpec
Target entity description: MSpec (Machine.Specifications) is a behavior-driven development testing framework for .NET that focuses on writing human-readable, specification-style tests.
  • A. MSO
    MSO is the three-letter FAA airport code for Missoula Montana Airport, a regional air transportation hub serving Missoula and western Montana.
  • B. MSO
    MSO is the National Rail station code for Moston railway station in Greater Manchester, England.
  • C. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • D. MSA
    MSA is the commonly used abbreviation for the Magnuson–Stevens Fishery Conservation and Management Act, the primary law governing marine fisheries management in U.S. federal waters.
  • E. MSA
    MSA is a common abbreviation for a metropolitan statistical area, a region defined by the U.S. Office of Management and Budget for statistical and demographic analysis.
  • 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de902d114881908a8f3c01b3c6d309 completed April 14, 2026, 7:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd551b006c8190b84449f2e2b59b62 completed May 8, 2026, 3:14 a.m.
NEDg Description generation batch_69fd55d90ed08190b6a0184715f39ff4 completed May 8, 2026, 3:17 a.m.
NED2 Entity disambiguation (via description) batch_69fd565d32fc8190acc1e733537a23cb completed May 8, 2026, 3:19 a.m.
Created at: April 10, 2026, 1:16 a.m.