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.