Triple

T3192138
Position Surface form Disambiguated ID Type / Status
Subject Coimbra-B railway station E66846 entity
Predicate hasStationCode P1289 FINISHED
Object CB
CB is the station code used to identify Coimbra-B railway station in Portugal’s rail network.
 E335344 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: CB | Statement: [Coimbra-B railway station, hasStationCode, CB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CB
Context triple: [Coimbra-B railway station, hasStationCode, CB]
  • A. CB
    CB is the post-nominal abbreviation indicating appointment as a Companion of the Order of the Bath, a British order of chivalry.
  • B. BC
    BC is the Canadian province of British Columbia, located on the west coast of Canada with Vancouver as its largest city.
  • C. BC
    BC is a private Jesuit research university located in Chestnut Hill, Massachusetts, known for its strong liberal arts programs and competitive NCAA Division I athletics.
  • D. SB
    SB was the communist-era secret police and intelligence service of the Polish People's Republic, known for surveillance, repression, and political control.
  • E. BB
    BB is the vehicle registration code used on license plates for the German town and district of Böblingen in the state of Baden-Württemberg.
  • 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: CB
Triple: [Coimbra-B railway station, hasStationCode, CB]
Generated description
CB is the station code used to identify Coimbra-B railway station in Portugal’s rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CB
Target entity description: CB is the station code used to identify Coimbra-B railway station in Portugal’s rail network.
  • A. CB
    CB is the post-nominal abbreviation indicating appointment as a Companion of the Order of the Bath, a British order of chivalry.
  • B. BC
    BC is a private Jesuit research university located in Chestnut Hill, Massachusetts, known for its strong liberal arts programs and competitive NCAA Division I athletics.
  • C. BC
    BC is the Canadian province of British Columbia, located on the west coast of Canada with Vancouver as its largest city.
  • D. SB
    SB was the communist-era secret police and intelligence service of the Polish People's Republic, known for surveillance, repression, and political control.
  • E. BB
    BB is the vehicle registration code used on license plates for the German town and district of Böblingen in the state of Baden-Württemberg.
  • 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_69ad8588ba18819086a10951c32ecb80 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada6e8e4b48190bc7c6443fc6da900 completed March 8, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24ba8a8b48190824dac45e37217cc completed March 12, 2026, 5:14 a.m.
NEDg Description generation batch_69b24d13ef908190b7b54d653e4d1ea4 completed March 12, 2026, 5:20 a.m.
NED2 Entity disambiguation (via description) batch_69b24db9af3881909d3d89ad985356c6 completed March 12, 2026, 5:23 a.m.
Created at: March 8, 2026, 3:07 p.m.