Triple

T1338426
Position Surface form Disambiguated ID Type / Status
Subject OV-chipkaart E28408 entity
Predicate usedByOperator P22525 FINISHED
Object RET
RET is a public transport company operating buses, trams, and metro services in and around Rotterdam in the Netherlands.
E153683 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: RET | Statement: [OV-chipkaart, usedByOperator, RET]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RET
Context triple: [OV-chipkaart, usedByOperator, RET]
  • A. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • B. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • C. RE
    RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
  • D. REN
    REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
  • E. DET
    DET is the standard NHL abbreviation for the Detroit Red Wings professional ice hockey team.
  • 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: RET
Triple: [OV-chipkaart, usedByOperator, RET]
Generated description
RET is a public transport company operating buses, trams, and metro services in and around Rotterdam in the Netherlands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: RET
Target entity description: RET is a public transport company operating buses, trams, and metro services in and around Rotterdam in the Netherlands.
  • A. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • B. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • C. RE
    RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
  • D. REN
    REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
  • E. DET
    DET is the standard NHL abbreviation for the Detroit Red Wings professional ice hockey team.
  • 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_69a49854eb3481908c7d56b2e449a290 completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c2115d388190b031ae2de1296f8a completed March 1, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc62e4c788190824df2a9b81692d7 completed March 8, 2026, 12:43 a.m.
NEDg Description generation batch_69acc6ae66408190bf48fe3150a08116 completed March 8, 2026, 12:45 a.m.
NED2 Entity disambiguation (via description) batch_69acc73b92248190b723cb64046799e3 completed March 8, 2026, 12:47 a.m.
Created at: March 1, 2026, 7:56 p.m.