Triple

T944386
Position Surface form Disambiguated ID Type / Status
Subject Museum station E20378 entity
Predicate hasAbbreviation P43 FINISHED
Object MUS
MUS is the standard abbreviation used to refer to Museum station, a public transit stop.
 E111047 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: MUS | Statement: [Museum station, hasAbbreviation, MUS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MUS
Context triple: [Museum station, hasAbbreviation, MUS]
  • A. MV
    MV is the vehicle registration code for the German federal state of Mecklenburg-Vorpommern.
  • B. USIC
    USIC is the commonly used acronym for the United States Intelligence Community, the federation of U.S. government agencies responsible for intelligence and national security activities.
  • C. MUR
    MUR is the Italian Ministry responsible for national policies on universities, higher education, and scientific and technological research.
  • D. Tunes
    Tunes is a town in Portugal’s Algarve region known as a key railway junction linking major lines in the south of the country.
  • E. MPS
    MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
  • 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: MUS
Triple: [Museum station, hasAbbreviation, MUS]
Generated description
MUS is the standard abbreviation used to refer to Museum station, a public transit stop.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MUS
Target entity description: MUS is the standard abbreviation used to refer to Museum station, a public transit stop.
  • A. MV
    MV is the vehicle registration code for the German federal state of Mecklenburg-Vorpommern.
  • B. USIC
    USIC is the commonly used acronym for the United States Intelligence Community, the federation of U.S. government agencies responsible for intelligence and national security activities.
  • C. MUR
    MUR is the Italian Ministry responsible for national policies on universities, higher education, and scientific and technological research.
  • D. Tunes
    Tunes is a town in Portugal’s Algarve region known as a key railway junction linking major lines in the south of the country.
  • E. MPS
    MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
  • 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_69a493b0270c81909e6c9ce310f6aa55 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b3a3ed3881908386af140477c514 completed March 1, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a826e585208190bf477bf78d162e84 completed March 4, 2026, 12:34 p.m.
NEDg Description generation batch_69a83365d590819085d8e92c1a69aa10 completed March 4, 2026, 1:28 p.m.
NED2 Entity disambiguation (via description) batch_69a834268c388190ac725f48be8f8ea6 completed March 4, 2026, 1:31 p.m.
Created at: March 1, 2026, 7:40 p.m.