Triple

T15554235
Position Surface form Disambiguated ID Type / Status
Subject Muni Metro lines E370826 entity
Predicate hasLineDesignation P974 FINISHED
Object M
M is a light rail line in San Francisco’s Muni Metro system that runs between the Embarcadero and the southwestern neighborhoods of the city.
E1163913 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: M | Statement: [Muni Metro lines, hasLineDesignation, M]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: M
Context triple: [Muni Metro lines, hasLineDesignation, M]
  • A. M
    M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
  • B. M
    M is the New York Stock Exchange ticker symbol for Macy's, Inc., a major American department store chain.
  • C. M
    M is an experimental musical composition by avant-garde American composer John Cage, reflecting his innovative approaches to sound and structure.
  • D. M
    M is a landmark 1931 German thriller film by Fritz Lang, renowned as an early and influential work in the serial killer and crime genre.
  • E. M
    "M" is a 1951 American crime thriller film directed by Joseph Losey, adapted from Fritz Lang’s 1931 classic, in which David Wayne portrays a hunted child murderer.
  • 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: M
Triple: [Muni Metro lines, hasLineDesignation, M]
Generated description
M is a light rail line in San Francisco’s Muni Metro system that runs between the Embarcadero and the southwestern neighborhoods of the city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: M
Target entity description: M is a light rail line in San Francisco’s Muni Metro system that runs between the Embarcadero and the southwestern neighborhoods of the city.
  • A. M
    M is a New York City Subway service that runs along the IND Sixth Avenue Line in Manhattan and connects Brooklyn and Queens.
  • B. M
    M is the common abbreviation for Sweden’s Moderate Party, a major center-right political party.
  • C. M
    M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
  • D. M
    M is the New York Stock Exchange ticker symbol for Macy's, Inc., a major American department store chain.
  • E. M
    M is the codename for James Bond’s stern and authoritative superior who heads the British Secret Service in the 007 franchise.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a96c0c88190808f68601a36b506 completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff456209288190aba6debd434af741 completed May 9, 2026, 2:32 p.m.
NEDg Description generation batch_69ff471cb68c8190924e894b190f15f4 completed May 9, 2026, 2:39 p.m.
NED2 Entity disambiguation (via description) batch_69ff47aeddac8190a87024019ecb1396 completed May 9, 2026, 2:41 p.m.
Created at: April 10, 2026, 4:09 a.m.