Triple

T1148072
Position Surface form Disambiguated ID Type / Status
Subject Charlie Y. Reader E23612 entity
Predicate hasFamilyName P18 FINISHED
Object Reader
Reader is a surname of English origin borne by various individuals, including Charlie Y. Reader.
 E61799 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: Reader | Statement: [Charlie Y. Reader, hasFamilyName, Reader]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Reader
Context triple: [Charlie Y. Reader, hasFamilyName, Reader]
  • A. Read
    Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
  • B. Reading
    Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
  • C. Reading
    Reading is a historic city in southeastern Pennsylvania known for its industrial heritage, transportation links, and role as a regional cultural and economic center.
  • D. Woman Reading
    Woman Reading is a painting by French artist Henri Matisse that exemplifies his use of bold color and simplified forms to depict an intimate, contemplative interior scene.
  • E. The Right to Read
    "The Right to Read" is a short story by Richard Stallman that warns about the dangers of restrictive digital rights management and the loss of freedoms in a future where sharing digital works is criminalized.
  • 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: Reader
Triple: [Charlie Y. Reader, hasFamilyName, Reader]
Generated description
Reader is a surname of English origin borne by various individuals, including Charlie Y. Reader.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Reader
Target entity description: Reader is a surname of English origin borne by various individuals, including Charlie Y. Reader.
  • A. Read chosen
    Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
  • B. Reading
    Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
  • C. Reading
    Reading is a historic city in southeastern Pennsylvania known for its industrial heritage, transportation links, and role as a regional cultural and economic center.
  • D. Woman Reading
    Woman Reading is a painting by French artist Henri Matisse that exemplifies his use of bold color and simplified forms to depict an intimate, contemplative interior scene.
  • E. The Right to Read
    "The Right to Read" is a short story by Richard Stallman that warns about the dangers of restrictive digital rights management and the loss of freedoms in a future where sharing digital works is criminalized.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc7041248190893e4c655dbd0604 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5eb3ec3881908c8cb39b422fcc71 completed March 7, 2026, 5:21 p.m.
NEDg Description generation batch_69ac5f248db081908596810839ee6160 completed March 7, 2026, 5:23 p.m.
NED2 Entity disambiguation (via description) batch_69ac5fb242488190bf99f63956aeda13 completed March 7, 2026, 5:26 p.m.
Created at: March 1, 2026, 7:44 p.m.