Triple

T2528888
Position Surface form Disambiguated ID Type / Status
Subject METRO light rail E56106 entity
Predicate systemBrand P39392 FINISHED
Object METRO E1376 NE FINISHED

How this triple was built (3 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: [METRO light rail, systemBrand, METRO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: METRO
Context triple: [METRO light rail, systemBrand, METRO]
  • A. Metro S.A.
    Metro S.A. is the state-owned company responsible for managing and operating the Santiago Metro system in Chile’s capital city.
  • B. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • C. Metro Insurgentes
    Metro Insurgentes is a major Mexico City Metro station on Line 1, located near the Glorieta de Insurgentes and serving as a key transit hub for the Roma and Zona Rosa areas.
  • D. Metro chosen
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: systemBrand
Context triple: [METRO light rail, systemBrand, METRO]
  • A. chipsetBrand
    Indicates the brand or manufacturer associated with a device’s chipset.
  • B. systemBoard
    Indicates a relationship where a system is associated with, mounted on, or implemented via a particular main circuit board (motherboard) that hosts its core components.
  • C. systemType
    Indicates the classification or category of a system that an entity belongs to or operates as.
  • D. biosVendor
    Indicates the vendor or manufacturer responsible for producing the BIOS firmware for a device or system.
  • E. manufacturerType
    Indicates the classification or category of a manufacturer based on its role, characteristics, or production type.
  • 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_69ab4a48e4f081908f1218d244608659 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd25903f08190b46e12d32278daca completed March 7, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2bb3b55c81909e72ed055887ecca completed March 9, 2026, 8:21 p.m.
PD Predicate disambiguation batch_69abd0c2e34c8190a914d5c2afba147c completed March 7, 2026, 7:16 a.m.
PDg Predicate description generation batch_69abd18e72a88190bdcf12b326d42fad completed March 7, 2026, 7:19 a.m.
Created at: March 6, 2026, 9:46 p.m.