Triple

T3710078
Position Surface form Disambiguated ID Type / Status
Subject Metropolitan Transit System E80988 entity
Predicate shortName P43 FINISHED
Object MTS
MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
 E381786 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: MTS | Statement: [Metropolitan Transit System, shortName, MTS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MTS
Context triple: [Metropolitan Transit System, shortName, MTS]
  • A. MTM
    MTM is the Mercury Transfer Module of the BepiColombo mission, responsible for propelling and guiding the spacecraft on its journey to Mercury.
  • B. MRTC
    MRTC is the Marine Raider Training Center, the primary U.S. Marine Corps Special Operations Command facility responsible for training and preparing Marine Raiders for special operations missions.
  • C. MFS
    MFS (Macintosh File System) is the original flat file system used by early Macintosh computers before the introduction of the hierarchical HFS.
  • D. 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.
  • E. MPS
    MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
  • 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: MTS
Triple: [Metropolitan Transit System, shortName, MTS]
Generated description
MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MTS
Target entity description: MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
  • A. MTM
    MTM is the Mercury Transfer Module of the BepiColombo mission, responsible for propelling and guiding the spacecraft on its journey to Mercury.
  • B. MRTC
    MRTC is the Marine Raider Training Center, the primary U.S. Marine Corps Special Operations Command facility responsible for training and preparing Marine Raiders for special operations missions.
  • C. MFS
    MFS (Macintosh File System) is the original flat file system used by early Macintosh computers before the introduction of the hierarchical HFS.
  • D. 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.
  • E. MPS
    MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
  • 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_69ad8b1793888190a5f70e4b21dc05a1 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adc584b86c8190ba1a1073da440b07 completed March 8, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4ce052660819089ad899a20b8a720 completed March 14, 2026, 2:55 a.m.
NEDg Description generation batch_69b4cf85b968819085dad34a80767984 completed March 14, 2026, 3:01 a.m.
NED2 Entity disambiguation (via description) batch_69b4cfecf2bc8190afb3bd8ebfd3cc64 completed March 14, 2026, 3:03 a.m.
Created at: March 8, 2026, 3:33 p.m.