Triple
T15121578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yummy |
E361183
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Daniel Hackett
Daniel Hackett is a writer known for his work associated with the project or publication "Yummy."
|
E1145908
|
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: Daniel Hackett | Statement: [Yummy, writer, Daniel Hackett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Hackett Context triple: [Yummy, writer, Daniel Hackett]
-
A.
Jonathan Hackett
Jonathan Hackett is an actor best known for his role in Lars von Trier’s acclaimed 1996 drama film "Breaking the Waves."
-
B.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
C.
Tim Hackett
Tim Hackett is a person notable enough to be specifically cited as a bearer of the surname Hackett, though detailed public information about him is limited.
-
D.
Andrew Dillin
Andrew Dillin is an American molecular biologist known for his influential research on the genetics of aging and protein homeostasis, particularly using C. elegans as a model organism.
-
E.
Ken Hutchison
Ken Hutchison was a Scottish actor known for his intense character roles in film and television during the 1970s and 1980s.
- 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: Daniel Hackett Triple: [Yummy, writer, Daniel Hackett]
Generated description
Daniel Hackett is a writer known for his work associated with the project or publication "Yummy."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Hackett Target entity description: Daniel Hackett is a writer known for his work associated with the project or publication "Yummy."
-
A.
Jonathan Hackett
Jonathan Hackett is an actor best known for his role in Lars von Trier’s acclaimed 1996 drama film "Breaking the Waves."
-
B.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
C.
Tim Hackett
Tim Hackett is a person notable enough to be specifically cited as a bearer of the surname Hackett, though detailed public information about him is limited.
-
D.
Andrew Dillin
Andrew Dillin is an American molecular biologist known for his influential research on the genetics of aging and protein homeostasis, particularly using C. elegans as a model organism.
-
E.
Ken Hutchison
Ken Hutchison was a Scottish actor known for his intense character roles in film and television during the 1970s and 1980s.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd24523081908cf12a5d6bdd634d |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fee0fdab548190b2ec90ae275411a8 |
completed | May 9, 2026, 7:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee1eb7b488190b1aef3bb2bc8b8ec |
completed | May 9, 2026, 7:27 a.m. |
Created at: April 10, 2026, 3:06 a.m.