Triple

T7620368
Position Surface form Disambiguated ID Type / Status
Subject Arrondissement of Antwerp E172475 entity
Predicate contains P35 FINISHED
Object Lint
Lint is a small municipality in the Belgian province of Antwerp, known for its residential character and proximity to the city of Antwerp.
E676441 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: Lint | Statement: [Arrondissement of Antwerp, contains, Lint]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lint
Context triple: [Arrondissement of Antwerp, contains, Lint]
  • A. lint
    lint is a static code analysis tool that detects potential errors, bugs, and style issues in C source code before compilation or execution.
  • B. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • C. Len
    Len is a common shortened form of the given name Leonard.
  • D. Lt
    Lt is the standard military abbreviation for the commissioned officer rank of Lieutenant in the Australian Army.
  • E. LT
    LT is a mid-level trim designation commonly used by Chevrolet to denote a better-equipped, more comfort- and feature-focused version of its vehicles.
  • 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: Lint
Triple: [Arrondissement of Antwerp, contains, Lint]
Generated description
Lint is a small municipality in the Belgian province of Antwerp, known for its residential character and proximity to the city of Antwerp.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lint
Target entity description: Lint is a small municipality in the Belgian province of Antwerp, known for its residential character and proximity to the city of Antwerp.
  • A. lint
    lint is a static code analysis tool that detects potential errors, bugs, and style issues in C source code before compilation or execution.
  • B. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • C. Len
    Len is a common shortened form of the given name Leonard.
  • D. Lt
    Lt is the standard military abbreviation for the commissioned officer rank of Lieutenant in the Australian Army.
  • E. LT
    LT is a mid-level trim designation commonly used by Chevrolet to denote a better-equipped, more comfort- and feature-focused version of its vehicles.
  • 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_69c699506b308190826894dab1d9ea86 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fa62870c8190b17f44eb7a3ff2ad completed March 27, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86874d5e48190a07c48f667fb8f6e completed March 28, 2026, 11:47 p.m.
NEDg Description generation batch_69c868d21b688190ba2f6ad849f4bde5 completed March 28, 2026, 11:48 p.m.
NED2 Entity disambiguation (via description) batch_69c869d14ae08190b2742af3878333e6 completed March 28, 2026, 11:52 p.m.
Created at: March 27, 2026, 3:55 p.m.