Triple

T2238740
Position Surface form Disambiguated ID Type / Status
Subject Abbott Laboratories E49343 entity
Predicate hasFormerDivision P10801 FINISHED
Object AbbVie E184183 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: AbbVie | Statement: [Abbott Laboratories, hasFormerDivision, AbbVie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AbbVie
Context triple: [Abbott Laboratories, hasFormerDivision, AbbVie]
  • A. AbbVie chosen
    AbbVie is a global biopharmaceutical company known for developing and marketing innovative therapies in areas such as immunology, oncology, and neuroscience.
  • B. Pfizer
    Pfizer is a major American multinational pharmaceutical and biotechnology corporation known for developing a wide range of prescription medicines and vaccines, including one of the first widely used COVID-19 vaccines.
  • C. Abbott Laboratories
    Abbott Laboratories is a global healthcare company that develops and manufactures medical devices, diagnostics, branded generic medicines, and nutritional products.
  • D. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • E. AstraZeneca
    AstraZeneca is a global biopharmaceutical company known for researching, developing, and manufacturing prescription medicines across areas such as oncology, cardiovascular, respiratory, and immunology.
  • 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_69a88aa84bdc819086df50e9c20b301e completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc0bb3fac81908b1e8518951dd160 completed March 7, 2026, 6:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b0a9ee881909dee8b0a657b9c73 completed March 9, 2026, 6:39 a.m.
Created at: March 4, 2026, 7:47 p.m.