Triple

T15526734
Position Surface form Disambiguated ID Type / Status
Subject Reading Power Station E369102 entity
Predicate hasComponent P35 FINISHED
Object Reading B
Reading B was a later-stage generating unit within the Reading Power Station complex, contributing additional electricity production capacity to the overall plant.
 E1161525 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 B | Statement: [Reading Power Station, hasComponent, Reading B]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Reading B
Context triple: [Reading Power Station, hasComponent, Reading B]
  • A. Reading
    Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
  • B. 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.
  • C. Reading
    "Reading" is an Impressionist painting by Berthe Morisot that depicts a quiet, intimate moment of a woman absorbed in a book.
  • D. Read
    Read is a village in Lancashire, England, situated near the River Calder and known for its residential community and local amenities.
  • E. Read
    Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
  • 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 B
Triple: [Reading Power Station, hasComponent, Reading B]
Generated description
Reading B was a later-stage generating unit within the Reading Power Station complex, contributing additional electricity production capacity to the overall plant.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Reading B
Target entity description: Reading B was a later-stage generating unit within the Reading Power Station complex, contributing additional electricity production capacity to the overall plant.
  • A. Reading
    Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
  • B. 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.
  • C. Reading
    "Reading" is an Impressionist painting by Berthe Morisot that depicts a quiet, intimate moment of a woman absorbed in a book.
  • D. Read
    Read is a village in Lancashire, England, situated near the River Calder and known for its residential community and local amenities.
  • E. Read
    Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
  • 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.