Triple

T4850369
Position Surface form Disambiguated ID Type / Status
Subject Pfizer E108398 entity
Predicate hasSubsidiary P254 FINISHED
Object Wyeth
Wyeth was a major American pharmaceutical and biotechnology company known for developing vaccines, prescription drugs, and consumer healthcare products before being acquired by Pfizer.
 E474926 NE FINISHED

How this triple was built (4 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: Wyeth | Statement: [Pfizer, hasSubsidiary, Wyeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wyeth
Context triple: [Pfizer, hasSubsidiary, Wyeth]
  • A. Kensett
    Kensett is the surname of John Frederick Kensett, a prominent 19th-century American landscape painter associated with the Hudson River School.
  • B. Abbott
    Abbott is a masculine given name of English origin, historically associated with clerical or religious roles.
  • C. Swanson
    Swanson is a well-known American food brand recognized for its canned broths, stocks, and frozen meals.
  • D. Schueller
    Schueller is a French surname most notably associated with Eugène Schueller, the chemist and entrepreneur who founded the cosmetics company L’Oréal.
  • E. Rinehart & Company
    Rinehart & Company was an American publishing house known for issuing notable mid-20th-century literary works, including major war novels and popular fiction.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wyeth
Triple: [Pfizer, hasSubsidiary, Wyeth]
Generated description
Wyeth was a major American pharmaceutical and biotechnology company known for developing vaccines, prescription drugs, and consumer healthcare products before being acquired by Pfizer.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wyeth
Target entity description: Wyeth was a major American pharmaceutical and biotechnology company known for developing vaccines, prescription drugs, and consumer healthcare products before being acquired by Pfizer.
  • A. Kensett
    Kensett is the surname of John Frederick Kensett, a prominent 19th-century American landscape painter associated with the Hudson River School.
  • B. Abbott
    Abbott is a masculine given name of English origin, historically associated with clerical or religious roles.
  • C. Swanson
    Swanson is a well-known American food brand recognized for its canned broths, stocks, and frozen meals.
  • D. Schueller
    Schueller is a French surname most notably associated with Eugène Schueller, the chemist and entrepreneur who founded the cosmetics company L’Oréal.
  • E. Rinehart & Company
    Rinehart & Company was an American publishing house known for issuing notable mid-20th-century literary works, including major war novels and popular fiction.
  • F. None of above. chosen

Provenance (5 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_69bd440a89548190a5f14ba6da6b97dc completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d1e5cf08190bd6b6a524748f170 completed March 20, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5cdefda8819095fbc04446bf32f5 completed March 21, 2026, 8:54 a.m.
NEDg Description generation batch_69be5dadcec88190bf9a272c4a9aef9a completed March 21, 2026, 8:58 a.m.
NED2 Entity disambiguation (via description) batch_69be6159ff7c8190baa116240f76dea5 completed March 21, 2026, 9:14 a.m.
Created at: March 20, 2026, 1:25 p.m.