Triple

T15526735
Position Surface form Disambiguated ID Type / Status
Subject Reading Power Station E369102 entity
Predicate hasComponent P35 FINISHED
Object Reading C
Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
E1161526 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: Reading C | Statement: [Reading Power Station, hasComponent, Reading C]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reading C
Context triple: [Reading Power Station, hasComponent, Reading C]
  • A. On to C (programming book)
    "On to C" is a programming book by Patrick Henry Winston that introduces and teaches the C language with an emphasis on clear explanations and practical examples for learners.
  • B. C
    C is a tram route designation used in the Strasbourg tramway network in France.
  • C. C
    C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
  • D. C
    C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
  • E. C
    C is one of the three central women in Edward Albee’s play "Three Tall Women," representing a younger stage of the protagonist’s life and perspective.
  • 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: Reading C
Triple: [Reading Power Station, hasComponent, Reading C]
Generated description
Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Reading C
Target entity description: Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
  • A. On to C (programming book)
    "On to C" is a programming book by Patrick Henry Winston that introduces and teaches the C language with an emphasis on clear explanations and practical examples for learners.
  • B. C
    C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
  • C. C
    C is a light rail service designation used by the Los Angeles Metro system for one of its primary rail lines.
  • D. C
    C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
  • E. C
    C is one of the three central women in Edward Albee’s play "Three Tall Women," representing a younger stage of the protagonist’s life and perspective.
  • 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_69d85a1794cc8190b0b428716296e63e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e04145178481909fb0339a79d4239e completed April 16, 2026, 1:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d598e6c8190870e9249197f5f53 completed May 9, 2026, 1:57 p.m.
NEDg Description generation batch_69ff3de663848190936a5b1d31d18c75 completed May 9, 2026, 2 p.m.
NED2 Entity disambiguation (via description) batch_69ff3e8c5f308190a335dbc45f9d88ee completed May 9, 2026, 2:02 p.m.
Created at: April 10, 2026, 4:05 a.m.