Triple

T20137498
Position Surface form Disambiguated ID Type / Status
Subject Seattle Times E491059 entity
Predicate hasSection P35 FINISHED
Object Metro
Metro is the local news section of The Seattle Times that focuses on regional and community coverage in and around the Seattle area.
E1413432 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: Metro | Statement: [Seattle Times, hasSection, Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metro
Context triple: [Seattle Times, hasSection, Metro]
  • A. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • B. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • C. Metro
    Metro is the professional alias of Metro Boomin, a prominent American record producer and DJ known for shaping the sound of modern hip-hop and trap music.
  • D. Metro
    Metro is the public transportation agency serving the St. Louis metropolitan area, operating bus, light rail, and paratransit services.
  • E. Metro
    Metro is the public transport brand used for bus and rail services across West Yorkshire, England.
  • 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: Metro
Triple: [Seattle Times, hasSection, Metro]
Generated description
Metro is the local news section of The Seattle Times that focuses on regional and community coverage in and around the Seattle area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Metro
Target entity description: Metro is the local news section of The Seattle Times that focuses on regional and community coverage in and around the Seattle area.
  • A. Metro
    Metro is a free, commuter-focused daily newspaper in the United Kingdom known for its concise coverage of news, entertainment, and lifestyle.
  • B. Metro
    Metro is the Los Angeles Police Department’s elite Metropolitan Division, known for handling specialized tactical operations, crowd control, and high-risk incidents.
  • C. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • D. Metro
    Metro is the public transportation agency serving the St. Louis metropolitan area, operating bus, light rail, and paratransit services.
  • E. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • 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_69da62651a0c8190a3e05e95e056a66b completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6676879f48190a59da04393d2a8cc completed April 20, 2026, 5:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a082de4bba88190a7d20a03826cbff0 completed May 16, 2026, 8:42 a.m.
NEDg Description generation batch_6a082ee3aafc819088a14b0a5d655072 completed May 16, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_6a082f70e24881909c43a85289df4976 completed May 16, 2026, 8:48 a.m.
Created at: April 11, 2026, 11:32 p.m.