Triple
T17686169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bodies |
E440892
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Roger Hurley
Roger Hurley is the central protagonist of the crime drama series "Bodies," around whom the show's primary investigation and narrative revolve.
|
E1348687
|
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: Roger Hurley | Statement: [Bodies, mainCharacter, Roger Hurley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roger Hurley Context triple: [Bodies, mainCharacter, Roger Hurley]
-
A.
Michael L. Hurley
Michael L. Hurley is a notable individual recognized for achievements significant enough to be associated with the surname Hurley.
-
B.
Ted Hurley
Ted Hurley is an Irish mathematician known for his work in algebra, particularly in group theory and coding theory.
-
C.
Michael W. Hurley
Michael W. Hurley is a notable individual recognized for achievements significant enough to be associated with the surname Hurley.
-
D.
Michael R. Hurley
Michael R. Hurley is a notable individual recognized for achievements or prominence significant enough to be specifically cited as a bearer of the Hurley name.
-
E.
Michael Bostick
Michael Bostick is a film producer known for his work on major Hollywood comedies and family films, including the hit movie "Bruce Almighty."
- 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: Roger Hurley Triple: [Bodies, mainCharacter, Roger Hurley]
Generated description
Roger Hurley is the central protagonist of the crime drama series "Bodies," around whom the show's primary investigation and narrative revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Roger Hurley Target entity description: Roger Hurley is the central protagonist of the crime drama series "Bodies," around whom the show's primary investigation and narrative revolve.
-
A.
Michael L. Hurley
Michael L. Hurley is a notable individual recognized for achievements significant enough to be associated with the surname Hurley.
-
B.
Ted Hurley
Ted Hurley is an Irish mathematician known for his work in algebra, particularly in group theory and coding theory.
-
C.
Michael W. Hurley
Michael W. Hurley is a notable individual recognized for achievements significant enough to be associated with the surname Hurley.
-
D.
Michael R. Hurley
Michael R. Hurley is a notable individual recognized for achievements or prominence significant enough to be specifically cited as a bearer of the Hurley name.
-
E.
Michael Bostick
Michael Bostick is a film producer known for his work on major Hollywood comedies and family films, including the hit movie "Bruce Almighty."
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47047d90c8190a172201f3de6db87 |
completed | April 19, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a058296efe481908e5a19acc0d0b738 |
completed | May 14, 2026, 8:06 a.m. |
| NEDg | Description generation | batch_6a05866776d881909723e694ab9a8674 |
completed | May 14, 2026, 8:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0586f85a448190becf166dbc584a1f |
completed | May 14, 2026, 8:25 a.m. |
Created at: April 10, 2026, 10:02 a.m.