Triple

T8245604
Position Surface form Disambiguated ID Type / Status
Subject Lycra E192841 entity
Predicate developedBy P73 FINISHED
Object DuPont E37316 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: DuPont | Statement: [Lycra, developedBy, DuPont]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DuPont
Context triple: [Lycra, developedBy, DuPont]
  • A. DuPont chosen
    DuPont is a major American chemical company historically known for pioneering materials science innovations and playing a key role in U.S. industrial and wartime production.
  • B. Rohm and Haas
    Rohm and Haas is a specialty chemicals company known for producing advanced materials and chemical products used in coatings, electronics, and industrial applications.
  • C. Du Pont
    Du Pont is a prominent American industrial and philanthropic family best known for founding the chemical company E.I. du Pont de Nemours and Company.
  • D. Dow Chemical Company
    Dow Chemical Company is a major American multinational chemical corporation known for producing a wide range of industrial, agricultural, and consumer chemical products.
  • E. DowDuPont
    DowDuPont was a large American chemical conglomerate formed by the merger of Dow Chemical and DuPont, later split into three independent companies focused on agriculture, materials science, and specialty products.
  • 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_69ca82de7b8c81908d8106f8a53cff9b completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7872f6d481909ea1d3c2aad1a2b1 completed March 31, 2026, 7:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf9fdde41081908de793672a889a67 completed April 3, 2026, 11:09 a.m.
Created at: March 30, 2026, 5:48 p.m.