Triple

T15312855
Position Surface form Disambiguated ID Type / Status
Subject AI2 E366082 entity
Predicate develops P73 FINISHED
Object Semantic Scholar E366084 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: Semantic Scholar | Statement: [AI2, develops, Semantic Scholar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Semantic Scholar
Context triple: [AI2, develops, Semantic Scholar]
  • A. Semantic Scholar chosen
    Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature more efficiently.
  • B. CiteSeerX
    CiteSeerX is a public digital library and search engine that focuses on indexing and providing access to scientific and academic research papers, particularly in computer and information science.
  • C. Microsoft Academic Search
    Microsoft Academic Search was a free academic search engine and research tool developed by Microsoft that indexed scholarly publications and provided citation analysis and visualization features.
  • D. DBLP
    DBLP is a comprehensive computer science bibliography database that indexes research papers, conference proceedings, and journals in the field of computing.
  • E. Google Scholar
    Google Scholar is a freely accessible academic search engine that indexes scholarly literature across many disciplines and formats, helping researchers find articles, theses, books, conference papers, and more.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd2d5a88190aead748920f93d47 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01e70a308190a7d6b91178c39bd3 completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:16 a.m.