Triple

T17091665
Position Surface form Disambiguated ID Type / Status
Subject Bayer cross E414740 entity
Predicate primaryText P2295 FINISHED
Object BAYER E85040 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: BAYER | Statement: [Bayer cross, primaryText, BAYER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BAYER
Context triple: [Bayer cross, primaryText, BAYER]
  • A. Bayer chosen
    Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
  • B. Ciba-Geigy
    Ciba-Geigy was a major Swiss pharmaceutical and chemical company that became one of the predecessors of Novartis after its merger with Sandoz in 1996.
  • C. Schering
    Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
  • D. Bayer Sager
    Bayer Sager is the surname of Carole Bayer Sager, an American songwriter known for numerous pop hits and award-winning collaborations.
  • E. Roche
    Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbfa09b08190be4303dd0d174feb completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01673ff7ec8190add7af932c38deef completed May 11, 2026, 5:21 a.m.
Created at: April 10, 2026, 5:35 a.m.