Triple

T8738298
Position Surface form Disambiguated ID Type / Status
Subject John Gardner (British writer) E207438 entity
Predicate notableWork P4 FINISHED
Object Madrigal
Madrigal is a novel by British author John Gardner that showcases his distinctive blend of literary sophistication and inventive storytelling.
E754773 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: Madrigal | Statement: [John Gardner (British writer), notableWork, Madrigal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrigal
Context triple: [John Gardner (British writer), notableWork, Madrigal]
  • A. Sorraia
    Sorraia is a river in central Portugal that flows through the Ribatejo region before joining the Tagus River.
  • B. Davila
    Davila is an Italian surname most notably associated with the 17th-century historian Enrico Caterino Davila.
  • C. Baltasar
    Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • D. Usnavi de la Vega
    Usnavi de la Vega is the bodega owner and central narrator of Lin-Manuel Miranda’s musical "In the Heights," whose story anchors the show’s portrait of a tight-knit Latino community in Washington Heights.
  • E. Albayzín
    Albayzín is the historic Moorish quarter of Granada, Spain, known for its narrow winding streets, whitewashed houses, and iconic views of the Alhambra.
  • 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: Madrigal
Triple: [John Gardner (British writer), notableWork, Madrigal]
Generated description
Madrigal is a novel by British author John Gardner that showcases his distinctive blend of literary sophistication and inventive storytelling.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Madrigal
Target entity description: Madrigal is a novel by British author John Gardner that showcases his distinctive blend of literary sophistication and inventive storytelling.
  • A. Sorraia
    Sorraia is a river in central Portugal that flows through the Ribatejo region before joining the Tagus River.
  • B. Davila
    Davila is an Italian surname most notably associated with the 17th-century historian Enrico Caterino Davila.
  • C. Baltasar
    Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • D. Usnavi de la Vega
    Usnavi de la Vega is the bodega owner and central narrator of Lin-Manuel Miranda’s musical "In the Heights," whose story anchors the show’s portrait of a tight-knit Latino community in Washington Heights.
  • E. Albayzín
    Albayzín is the historic Moorish quarter of Granada, Spain, known for its narrow winding streets, whitewashed houses, and iconic views of the Alhambra.
  • 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_69ca835a03a081909d4d4cd01a18c9fb completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d470c8c81909ead395ef704c6ba completed March 31, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf42d5dd508190854fbbc2541aa819 completed April 3, 2026, 4:32 a.m.
NEDg Description generation batch_69cf440051bc8190ad9d649150187932 completed April 3, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_69cf4473ee0081908ed22eb0d855d7dd completed April 3, 2026, 4:39 a.m.
Created at: March 30, 2026, 6:38 p.m.