Triple

T10019262
Position Surface form Disambiguated ID Type / Status
Subject Jakarta Expression Language E200573 entity
Predicate formerlyKnownAs P65 FINISHED
Object Expression Language E200573 NE FINISHED

How this triple was built (2 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: Expression Language | Statement: [Jakarta Expression Language, formerlyKnownAs, Expression Language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Expression Language
Context triple: [Jakarta Expression Language, formerlyKnownAs, Expression Language]
  • A. Jakarta Expression Language chosen
    Jakarta Expression Language is a Java-based expression language used in Jakarta EE to simplify access to application data and logic within web and enterprise components.
  • B. Express
    "Express" is a popular song from the film and stage musical "Burlesque," known for its sultry style and association with Christina Aguilera’s performance.
  • C. Express
    Express is a minimalist and flexible web application framework for Node.js, widely used for building APIs and server-side applications.
  • D. Express
    Express is the nickname of the Windsor Express, a professional basketball team based in Windsor, Ontario, Canada.
  • E. Express
    Express is an American fashion retailer known for its trendy, youth-oriented apparel and accessories sold through mall-based stores and online.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca831c45f08190ac1505cc15076608 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd4f3a988190892cc698109be8b8 completed April 2, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26aaa38188190aed8c18eccd8a79d completed April 5, 2026, 1:59 p.m.
Created at: March 30, 2026, 8:53 p.m.