Triple

T12115720
Position Surface form Disambiguated ID Type / Status
Subject Amanda Abbington E288554 entity
Predicate notableWork P4 FINISHED
Object Cuffs
Cuffs is a British television police drama series that follows the professional and personal lives of frontline officers in Brighton.
 E965726 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: Cuffs | Statement: [Amanda Abbington, notableWork, Cuffs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cuffs
Context triple: [Amanda Abbington, notableWork, Cuffs]
  • A. Cuff It
    "Cuff It" is a disco- and funk-inspired R&B song by Beyoncé from her 2022 album "Renaissance," celebrated for its upbeat groove and feel-good party vibe.
  • B. Scarf
    Scarf is a long piece of fabric worn around the neck, head, or shoulders for warmth, sun protection, cleanliness, fashion, or religious reasons.
  • C. Garter
    The Garter is a prestigious emblem associated with the Order of the Garter, one of the highest orders of chivalry in the United Kingdom, traditionally worn by royal knights and ladies.
  • D. Pockets
    Pockets is a music producer known for contributing to Mos Def’s influential hip-hop album "Black on Both Sides."
  • E. Underscar
    Underscar is a locality on the lower slopes of Skiddaw in England’s Lake District, known as a common starting point for ascents of the mountain.
  • 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: Cuffs
Triple: [Amanda Abbington, notableWork, Cuffs]
Generated description
Cuffs is a British television police drama series that follows the professional and personal lives of frontline officers in Brighton.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cuffs
Target entity description: Cuffs is a British television police drama series that follows the professional and personal lives of frontline officers in Brighton.
  • A. Cuff It
    "Cuff It" is a disco- and funk-inspired R&B song by Beyoncé from her 2022 album "Renaissance," celebrated for its upbeat groove and feel-good party vibe.
  • B. Scarf
    Scarf is a long piece of fabric worn around the neck, head, or shoulders for warmth, sun protection, cleanliness, fashion, or religious reasons.
  • C. Garter
    The Garter is a prestigious emblem associated with the Order of the Garter, one of the highest orders of chivalry in the United Kingdom, traditionally worn by royal knights and ladies.
  • D. Pockets
    Pockets is a music producer known for contributing to Mos Def’s influential hip-hop album "Black on Both Sides."
  • E. Underscar
    Underscar is a locality on the lower slopes of Skiddaw in England’s Lake District, known as a common starting point for ascents of the mountain.
  • 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_69d6ab4a5c448190a110d1273314b21a completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9156921dc8190aa132b0ab3a7c184 completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f67f9c30819089305d5d42210c34 completed May 2, 2026, 1:05 p.m.
NEDg Description generation batch_69f5fdea1afc8190b39557fdc571e300 completed May 2, 2026, 1:36 p.m.
NED2 Entity disambiguation (via description) batch_69f5feeaf2e48190995f282b02a9caaf completed May 2, 2026, 1:40 p.m.
Created at: April 8, 2026, 9:49 p.m.