Triple

T16769796
Position Surface form Disambiguated ID Type / Status
Subject Hoechst industrial site E407561 entity
Predicate hasTenant P3277 FINISHED
Object Sanofi E133715 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: Sanofi | Statement: [Hoechst industrial site, hasTenant, Sanofi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sanofi
Context triple: [Hoechst industrial site, hasTenant, Sanofi]
  • A. Sanofi chosen
    Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
  • B. Roche
    Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • E. Bristol Myers Squibb
    Bristol Myers Squibb is a global biopharmaceutical company known for developing and manufacturing innovative medicines in areas such as oncology, immunology, and cardiovascular disease.
  • 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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b0356a9c8190b316cd00223e7537 completed April 18, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00a533e83481909966a7b86c8c8e64 completed May 10, 2026, 3:33 p.m.
Created at: April 10, 2026, 5:21 a.m.