Triple

T7292199
Position Surface form Disambiguated ID Type / Status
Subject Odenton station E164423 entity
Predicate hasStationCode P1289 FINISHED
Object ODN (MARC)
ODN (MARC) is the station code used by the MARC commuter rail system to identify Odenton station in Maryland.
 E16027 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: ODN (MARC) | Statement: [Odenton station, hasStationCode, ODN (MARC)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ODN (MARC)
Context triple: [Odenton station, hasStationCode, ODN (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
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • C. ODF
    ODF is the state agency responsible for managing Oregon’s forest resources, including wildfire protection, forest health, and sustainable timber management.
  • D. 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.
  • E. ODS
    ODS is the United Nations’ online platform for publishing, accessing, and searching official UN documents in multiple languages.
  • 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: ODN (MARC)
Triple: [Odenton station, hasStationCode, ODN (MARC)]
Generated description
ODN (MARC) is the station code used by the MARC commuter rail system to identify Odenton station in Maryland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ODN (MARC)
Target entity description: ODN (MARC) is the station code used by the MARC commuter rail system to identify Odenton station in Maryland.
  • 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. ODF
    ODF is the state agency responsible for managing Oregon’s forest resources, including wildfire protection, forest health, and sustainable timber management.
  • D. 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.
  • E. ODS
    ODS is the United Nations’ online platform for publishing, accessing, and searching official UN documents in multiple languages.
  • F. None of above.

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_69c6887a499881909dd23341399c59d8 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6eb6fc5788190b1b339d051f93c22 completed March 27, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e53ab41c8190b081e90fa6a1145c completed March 28, 2026, 2:27 p.m.
NEDg Description generation batch_69c7e6671e2c8190aed42aa673540efa completed March 28, 2026, 2:32 p.m.
NED2 Entity disambiguation (via description) batch_69c7e6cd820881909ef8fd3bc28d2716 completed March 28, 2026, 2:33 p.m.
Created at: March 27, 2026, 3 p.m.