Triple

T7780710
Position Surface form Disambiguated ID Type / Status
Subject Belsize Park tube station E221507 entity
Predicate hasStationCode P1289 FINISHED
Object BZP
BZP is the three-letter station code used to identify Belsize Park tube station on the London Underground network.
 E693232 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: BZP | Statement: [Belsize Park tube station, hasStationCode, BZP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BZP
Context triple: [Belsize Park tube station, hasStationCode, BZP]
  • A. BZ
    BZ is the vehicle registration code used on license plates for the municipality of Rammenau in Germany.
  • B. BZ
    BZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Belize.
  • C. BZ
    BZ is the commonly used abbreviation for the Ministry of Foreign Affairs of the Netherlands, which is responsible for the country’s foreign policy and international relations.
  • D. BZD
    BZD is the currency code for the Belize dollar, the official monetary unit of Belize.
  • E. BZV
    BZV is the IATA airport code for Maya-Maya Airport, the main international airport serving Brazzaville in the Republic of the Congo.
  • 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: BZP
Triple: [Belsize Park tube station, hasStationCode, BZP]
Generated description
BZP is the three-letter station code used to identify Belsize Park tube station on the London Underground network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BZP
Target entity description: BZP is the three-letter station code used to identify Belsize Park tube station on the London Underground network.
  • A. BZ
    BZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Belize.
  • B. BZ
    BZ is the commonly used abbreviation for the Ministry of Foreign Affairs of the Netherlands, which is responsible for the country’s foreign policy and international relations.
  • C. BZ
    BZ is the vehicle registration code used on license plates for the municipality of Rammenau in Germany.
  • D. BZD
    BZD is the currency code for the Belize dollar, the official monetary unit of Belize.
  • E. BZV
    BZV is the IATA airport code for Maya-Maya Airport, the main international airport serving Brazzaville in the Republic of the Congo.
  • 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_69ca83ebbef881909ac47f789145fef7 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69caa4d6cf9881909f5220437db13cc7 completed March 30, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf5c1f20c81908ae2fc35550b91b5 completed March 30, 2026, 10:14 p.m.
NEDg Description generation batch_69caf81d934881908fa41ebd43f3b2e2 completed March 30, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69cafa013f348190a2067dee4a0c8c40 completed March 30, 2026, 10:32 p.m.
Created at: March 30, 2026, 4:20 p.m.