Triple

T10156414
Position Surface form Disambiguated ID Type / Status
Subject Applied Materials E233786 entity
Predicate competesWith P1375 FINISHED
Object Lam Research E153652 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: Lam Research | Statement: [Applied Materials, competesWith, Lam Research]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lam Research
Context triple: [Applied Materials, competesWith, Lam Research]
  • A. Lam Research chosen
    Lam Research is a leading American semiconductor equipment company that supplies wafer fabrication tools and services to chip manufacturers worldwide.
  • B. Applied Materials
    Applied Materials is a leading American corporation that supplies equipment, services, and software for the manufacture of semiconductors and other advanced electronic components.
  • C. Sematech
    Sematech is a former U.S.-based semiconductor industry consortium that played a key role in advancing chip manufacturing technologies and standards through collaborative research among major chipmakers and government agencies.
  • D. Asml
    Asml is the rail code used to identify Amstelstation in the Dutch railway network.
  • E. Tokyo Electron
    Tokyo Electron is a leading Japanese manufacturer of semiconductor production equipment and related technologies, serving major chipmakers worldwide.
  • 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_69ca848e80748190b91d1e04d35512c7 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec3c47dc81909679903e6024eb49 completed April 2, 2026, 4:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d300a40638819082e575d957711377 completed April 6, 2026, 12:39 a.m.
Created at: March 30, 2026, 9:09 p.m.