Triple

T11397271
Position Surface form Disambiguated ID Type / Status
Subject Bayer 04 Leverkusen E270008 entity
Predicate parentCompany P254 FINISHED
Object Bayer AG 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 AG | Statement: [Bayer 04 Leverkusen, parentCompany, Bayer AG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bayer AG
Context triple: [Bayer 04 Leverkusen, parentCompany, Bayer AG]
  • 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. Boehringer Ingelheim
    Boehringer Ingelheim is a major German research-driven pharmaceutical company known for developing prescription medicines, animal health products, and biopharmaceuticals worldwide.
  • C. BASF
    BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
  • D. Schering
    Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
  • E. 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.
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d80019d3d48190a2f473deb6eae33a completed April 9, 2026, 7:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e8cc338081908da977b5b7c6bef3 completed April 20, 2026, 8:50 a.m.
Created at: April 8, 2026, 9:34 p.m.