Triple

T21198246
Position Surface form Disambiguated ID Type / Status
Subject Minneapolis and St. Louis Railway E522381 entity
Predicate abbreviation P43 FINISHED
Object M&StL
M&StL was a regional American railroad that primarily served the Midwest, connecting Minneapolis and St. Louis with surrounding agricultural and industrial areas.
 E1471866 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&StL | Statement: [Minneapolis and St. Louis Railway, abbreviation, M&StL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: M&StL
Context triple: [Minneapolis and St. Louis Railway, abbreviation, M&StL]
  • A. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • B. Muni
    Muni was an Indian character actor known for his supporting roles in mid-20th-century cinema.
  • C. Muni
    Muni is an honorific title traditionally used in Indian culture to denote a sage, seer, or revered spiritual teacher.
  • D. Muni
    Muni is San Francisco’s primary public transit agency, operating buses, light rail, historic streetcars, and the city’s iconic cable cars.
  • E. MAAS
    MAAS (Metal as a Service) is Canonical Ltd.'s open-source tool for provisioning, managing, and automating bare-metal servers at scale, often used in cloud and data center environments.
  • 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&StL
Triple: [Minneapolis and St. Louis Railway, abbreviation, M&StL]
Generated description
M&StL was a regional American railroad that primarily served the Midwest, connecting Minneapolis and St. Louis with surrounding agricultural and industrial areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: M&StL
Target entity description: M&StL was a regional American railroad that primarily served the Midwest, connecting Minneapolis and St. Louis with surrounding agricultural and industrial areas.
  • A. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • B. Muni
    Muni was an Indian character actor known for his supporting roles in mid-20th-century cinema.
  • C. Muni
    Muni is an honorific title traditionally used in Indian culture to denote a sage, seer, or revered spiritual teacher.
  • D. Muni
    Muni is San Francisco’s primary public transit agency, operating buses, light rail, historic streetcars, and the city’s iconic cable cars.
  • E. MAAS
    MAAS (Metal as a Service) is Canonical Ltd.'s open-source tool for provisioning, managing, and automating bare-metal servers at scale, often used in cloud and data center environments.
  • 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_69e0b51061388190aa03f19700d3ef04 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7333d6dec8190bbc66a71b31ea559 completed April 21, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a097ecd1d1c819081a3a301701a11ae completed May 17, 2026, 8:39 a.m.
NEDg Description generation batch_6a09807278ac8190ae2835ce6d79a9cc completed May 17, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_6a09810158948190a9504d50c964efd9 completed May 17, 2026, 8:49 a.m.
Created at: April 16, 2026, 3:16 p.m.