Triple

T6979880
Position Surface form Disambiguated ID Type / Status
Subject West Baltimore station E161813 entity
Predicate fareSystem P395 FINISHED
Object MARC E16027 NE FINISHED

How this triple was built (2 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: MARC | Statement: [West Baltimore station, fareSystem, MARC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MARC
Context triple: [West Baltimore station, fareSystem, MARC]
  • A. MARC
    MARC is a regional planning and coordination agency serving the Kansas City metropolitan area, focusing on transportation, emergency services, environmental planning, and community development.
  • B. MARC chosen
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • C. MARC standards
    MARC standards are a set of bibliographic data formats used worldwide to structure and exchange library catalog information in a consistent, machine-readable way.
  • D. Maschinelles Austauschformat für Bibliotheken
    Maschinelles Austauschformat für Bibliotheken (MAB) is a German machine-readable data exchange format historically used by libraries to encode and share bibliographic and authority records.
  • E. Korean MARC
    Korean MARC is a national bibliographic metadata standard used in South Korea for cataloging library and information resources.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c68855dc0481909b4c7e9e9ed273db completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db6c1efc8190ab1575ae2ce726db completed March 27, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a0e34288190ad2decbc18190c6b completed March 28, 2026, 5:41 a.m.
Created at: March 27, 2026, 2:31 p.m.