Triple

T2120878
Position Surface form Disambiguated ID Type / Status
Subject Greg Abbott E43918 entity
Predicate familyName P18 FINISHED
Object Abbott
Abbott is a common English surname borne by numerous notable individuals across politics, entertainment, science, and other fields.
 E49343 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: Abbott | Statement: [Greg Abbott, familyName, Abbott]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Abbott
Context triple: [Greg Abbott, familyName, Abbott]
  • A. Abbott Laboratories
    Abbott Laboratories is a global healthcare company that develops and manufactures medical devices, diagnostics, branded generic medicines, and nutritional products.
  • B. Baxter
    Baxter is a surname and given name of English and Scottish origin, historically associated with the occupation of a baker.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Behring
    Behring is a German surname most notably associated with Emil Adolf von Behring, the pioneering physiologist and first Nobel laureate in Physiology or Medicine for his work on serum therapy.
  • E. Schueller
    Schueller is a French surname most notably associated with Eugène Schueller, the chemist and entrepreneur who founded the cosmetics company L’Oréal.
  • 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: Abbott
Triple: [Greg Abbott, familyName, Abbott]
Generated description
Abbott is a common English surname borne by numerous notable individuals across politics, entertainment, science, and other fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Abbott
Target entity description: Abbott is a common English surname borne by numerous notable individuals across politics, entertainment, science, and other fields.
  • A. Abbott Laboratories chosen
    Abbott Laboratories is a global healthcare company that develops and manufactures medical devices, diagnostics, branded generic medicines, and nutritional products.
  • B. Baxter
    Baxter is a surname and given name of English and Scottish origin, historically associated with the occupation of a baker.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Behring
    Behring is a German surname most notably associated with Emil Adolf von Behring, the pioneering physiologist and first Nobel laureate in Physiology or Medicine for his work on serum therapy.
  • E. Schueller
    Schueller is a French surname most notably associated with Eugène Schueller, the chemist and entrepreneur who founded the cosmetics company L’Oréal.
  • F. None of above.

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_69a88717cfe48190b7ecdd68c824848a completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abbb3404348190bc843022fbd2b4d0 completed March 7, 2026, 5:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5197259c8190bffbbb4abaaddfd0 completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae532d39d8819097ae5826b42f92b2 completed March 9, 2026, 4:57 a.m.
NED2 Entity disambiguation (via description) batch_69ae539f7f008190b5f9d15fb3d362d9 completed March 9, 2026, 4:59 a.m.
Created at: March 4, 2026, 7:44 p.m.