Triple

T77214
Position Surface form Disambiguated ID Type / Status
Subject Frances Arnold E1543 entity
Predicate familyName P18 FINISHED
Object Arnold
Arnold is a common English and German surname borne by numerous notable individuals across fields such as science, politics, sports, and the arts.
 E22137 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: Arnold | Statement: [Frances Arnold, familyName, Arnold]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arnold
Context triple: [Frances Arnold, familyName, Arnold]
  • A. Arnold Vinnius
    Arnold Vinnius was a prominent 17th-century Dutch jurist and legal scholar whose influential commentaries helped shape the development and teaching of Roman-Dutch law.
  • B. Andrew
    Andrew is a masculine given name of Greek origin meaning "manly" or "brave," widely used in English-speaking countries and beyond.
  • C. Andrew
    Andrew is a subway station in South Boston on the Massachusetts Bay Transportation Authority's Red Line.
  • D. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • E. Bader
    Bader is the maiden surname of Ruth Bader Ginsburg, the late U.S. Supreme Court Justice and pioneering advocate for gender equality.
  • 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: Arnold
Triple: [Frances Arnold, familyName, Arnold]
Generated description
Arnold is a common English and German surname borne by numerous notable individuals across fields such as science, politics, sports, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arnold
Target entity description: Arnold is a common English and German surname borne by numerous notable individuals across fields such as science, politics, sports, and the arts.
  • A. Arnold Vinnius
    Arnold Vinnius was a prominent 17th-century Dutch jurist and legal scholar whose influential commentaries helped shape the development and teaching of Roman-Dutch law.
  • B. Andrew
    Andrew is a masculine given name of Greek origin meaning "manly" or "brave," widely used in English-speaking countries and beyond.
  • C. Andrew
    Andrew is a subway station in South Boston on the Massachusetts Bay Transportation Authority's Red Line.
  • D. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • E. Bader
    Bader is the maiden surname of Ruth Bader Ginsburg, the late U.S. Supreme Court Justice and pioneering advocate for gender equality.
  • 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_69a24c60d19c8190a1b6c105ca59ef5b completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a24f1d20b88190b66836cc018e52e1 completed Feb. 28, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2eb7495ac819093ffa2a622cbd71e completed Feb. 28, 2026, 1:19 p.m.
NEDg Description generation batch_69a2ec72092c8190bcc1efa7c0644560 completed Feb. 28, 2026, 1:24 p.m.
NED2 Entity disambiguation (via description) batch_69a2ecba99cc8190a85dd5e9c531a6e3 completed Feb. 28, 2026, 1:25 p.m.
Created at: Feb. 28, 2026, 2:06 a.m.