Triple

T23237329
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Fones E581338 entity
Predicate familyName P18 FINISHED
Object Fones
Fones is the surname historically associated with Elizabeth Fones, a 17th-century English colonist linked to early American settlement and the Winthrop family.
 E1578958 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: Fones | Statement: [Elizabeth Fones, familyName, Fones]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fones
Context triple: [Elizabeth Fones, familyName, Fones]
  • A. FONES
    FONES is the Swiss federal agency responsible for ensuring the country’s economic preparedness and securing vital supplies in times of crisis.
  • B. Telefomin
    Telefomin is a remote highland town in Papua New Guinea known for its rugged terrain, traditional cultures, and limited accessibility.
  • C. Fitel
    Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
  • D. Fon
    Fon is a major Gbe language of West Africa, primarily spoken by the Fon people in Benin and neighboring countries.
  • E. Fon
    Fon is the hereditary monarch and spiritual leader of many traditional kingdoms in Cameroon’s Grassfields region, particularly among groups such as the Bamileke.
  • 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: Fones
Triple: [Elizabeth Fones, familyName, Fones]
Generated description
Fones is the surname historically associated with Elizabeth Fones, a 17th-century English colonist linked to early American settlement and the Winthrop family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fones
Target entity description: Fones is the surname historically associated with Elizabeth Fones, a 17th-century English colonist linked to early American settlement and the Winthrop family.
  • A. FONES
    FONES is the Swiss federal agency responsible for ensuring the country’s economic preparedness and securing vital supplies in times of crisis.
  • B. Telefomin
    Telefomin is a remote highland town in Papua New Guinea known for its rugged terrain, traditional cultures, and limited accessibility.
  • C. Fitel
    Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
  • D. Fon
    Fon is a major Gbe language of West Africa, primarily spoken by the Fon people in Benin and neighboring countries.
  • E. Fon
    Fon is the hereditary monarch and spiritual leader of many traditional kingdoms in Cameroon’s Grassfields region, particularly among groups such as the Bamileke.
  • 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_69e2460556f88190be1744a84a84173f completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f192ea590c81908cd677d89f67d49f completed April 29, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c3f5c2004819086c7e7c629e33b67 completed May 19, 2026, 10:45 a.m.
NEDg Description generation batch_6a0c4028b44c8190bde6e2c76b5f1587 completed May 19, 2026, 10:49 a.m.
NED2 Entity disambiguation (via description) batch_6a0c4367753c8190a4f6dbf2afd12b76 completed May 19, 2026, 11:03 a.m.
Created at: April 17, 2026, 4:09 p.m.