Triple

T3315338
Position Surface form Disambiguated ID Type / Status
Subject The Silence E69667 entity
Predicate hasCharacter P2308 FINISHED
Object Martin Dekker
Martin Dekker is a fictional character appearing in the Doctor Who audio drama "The Silence."
 E346546 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: Martin Dekker | Statement: [The Silence, hasCharacter, Martin Dekker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martin Dekker
Context triple: [The Silence, hasCharacter, Martin Dekker]
  • A. Tim Kruithoff
    Tim Kruithoff is a German local politician who serves as the mayor of the city of Emden in Lower Saxony.
  • B. Christian Huitema
    Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
  • C. Sander Dieleman
    Sander Dieleman is a machine learning researcher known for his influential work in deep learning for audio and music, including contributions to models such as WaveNet.
  • D. Marc de Jonge
    Marc de Jonge was a French actor best known internationally for playing the Soviet Colonel Zaysen in the action film "Rambo III."
  • E. Alex Reedijk
    Alex Reedijk is a New Zealand-born arts administrator best known for serving as General Director of Scottish Opera, where he has played a key role in shaping the company’s artistic direction and operations.
  • 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: Martin Dekker
Triple: [The Silence, hasCharacter, Martin Dekker]
Generated description
Martin Dekker is a fictional character appearing in the Doctor Who audio drama "The Silence."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martin Dekker
Target entity description: Martin Dekker is a fictional character appearing in the Doctor Who audio drama "The Silence."
  • A. Tim Kruithoff
    Tim Kruithoff is a German local politician who serves as the mayor of the city of Emden in Lower Saxony.
  • B. Christian Huitema
    Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
  • C. Sander Dieleman
    Sander Dieleman is a machine learning researcher known for his influential work in deep learning for audio and music, including contributions to models such as WaveNet.
  • D. Marc de Jonge
    Marc de Jonge was a French actor best known internationally for playing the Soviet Colonel Zaysen in the action film "Rambo III."
  • E. Alex Reedijk
    Alex Reedijk is a New Zealand-born arts administrator best known for serving as General Director of Scottish Opera, where he has played a key role in shaping the company’s artistic direction and operations.
  • 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_69ad85a0bb048190a5458d2738012d61 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb110b28081909b366623e3b0783d completed March 8, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3fa347481909432bd0178e7ea57 completed March 12, 2026, 5:12 p.m.
NEDg Description generation batch_69b2faf56ca08190b93486c07d94c08e completed March 12, 2026, 5:42 p.m.
NED2 Entity disambiguation (via description) batch_69b312b902fc819081ec01c3d418fae0 completed March 12, 2026, 7:23 p.m.
Created at: March 8, 2026, 3:11 p.m.