Triple

T5923796
Position Surface form Disambiguated ID Type / Status
Subject pub.dev E131756 entity
Predicate operatedBy P86 FINISHED
Object Flutter team E131750 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: Flutter team | Statement: [pub.dev, operatedBy, Flutter team]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flutter team
Context triple: [pub.dev, operatedBy, Flutter team]
  • A. Angular Team at Google
    The Angular Team at Google is the core group of engineers and designers responsible for developing, evolving, and supporting the Angular web application framework.
  • B. Flutter chosen
    Flutter is an open-source UI toolkit by Google for building natively compiled, cross-platform applications from a single Dart codebase.
  • C. Dart
    Dart is a client-optimized, object-oriented programming language developed by Google, primarily used for building web and cross-platform mobile applications (notably with the Flutter framework).
  • D. Microsoft TypeScript team
    The Microsoft TypeScript team is the group at Microsoft responsible for designing, developing, and maintaining the TypeScript programming language and its ecosystem.
  • E. Habana Labs
    Habana Labs is an Israeli-based company specializing in artificial intelligence accelerators and deep learning processors for data centers.
  • 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_69c0085a1ed08190a7e9a8b6323fd680 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c03851189c819094524e8b5080545e completed March 22, 2026, 6:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c0483e3481908e50f8b34b11a878 completed March 23, 2026, 4:23 a.m.
Created at: March 22, 2026, 4 p.m.