Triple
T20006931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zen |
E494483
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Michael Dibdin
Michael Dibdin was a British crime novelist best known for his Aurelio Zen detective series set in Italy.
|
E1405959
|
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: Michael Dibdin | Statement: [Zen, creator, Michael Dibdin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Dibdin Context triple: [Zen, creator, Michael Dibdin]
-
A.
Mark Bazeley
Mark Bazeley is a British actor known for his work in film, television, and theatre, including roles in political dramas and high-profile UK series.
-
B.
Douglas Langdale
Douglas Langdale is an American television and film writer best known for his work on animated projects such as The Book of Life and various popular animated series.
-
C.
Jonathan Gledhill
Jonathan Gledhill was an English Anglican bishop who served in senior episcopal roles in the Church of England, including as Bishop of Stafford.
-
D.
Matt Strevens
Matt Strevens is a British television producer best known for his work as an executive producer on the revived era of Doctor Who.
-
E.
Ian Norden
Ian Norden is a blockchain developer known for co-authoring Ethereum Improvement Proposal EIP-1559, which reformed Ethereum’s transaction fee mechanism.
- 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: Michael Dibdin Triple: [Zen, creator, Michael Dibdin]
Generated description
Michael Dibdin was a British crime novelist best known for his Aurelio Zen detective series set in Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Dibdin Target entity description: Michael Dibdin was a British crime novelist best known for his Aurelio Zen detective series set in Italy.
-
A.
Mark Bazeley
Mark Bazeley is a British actor known for his work in film, television, and theatre, including roles in political dramas and high-profile UK series.
-
B.
Douglas Langdale
Douglas Langdale is an American television and film writer best known for his work on animated projects such as The Book of Life and various popular animated series.
-
C.
Jonathan Gledhill
Jonathan Gledhill was an English Anglican bishop who served in senior episcopal roles in the Church of England, including as Bishop of Stafford.
-
D.
Matt Strevens
Matt Strevens is a British television producer best known for his work as an executive producer on the revived era of Doctor Who.
-
E.
Ian Norden
Ian Norden is a blockchain developer known for co-authoring Ethereum Improvement Proposal EIP-1559, which reformed Ethereum’s transaction fee mechanism.
- 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a648a88190853ee741edcf6ca2 |
completed | April 20, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08050fcc088190a839c529cda3f588 |
completed | May 16, 2026, 5:47 a.m. |
| NEDg | Description generation | batch_6a08061f6aa08190a7c8b4fc55ea1400 |
completed | May 16, 2026, 5:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a080706231c819081499808132fad82 |
completed | May 16, 2026, 5:56 a.m. |
Created at: April 11, 2026, 3:33 p.m.