Triple
T15315552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc Martin |
E366146
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
PC Joe Penhale
PC Joe Penhale is a bumbling yet well-meaning village police officer in the British television series "Doc Martin."
|
E1149673
|
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: PC Joe Penhale | Statement: [Doc Martin, hasCharacter, PC Joe Penhale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PC Joe Penhale Context triple: [Doc Martin, hasCharacter, PC Joe Penhale]
-
A.
Pat Proft
Pat Proft is an American comedy writer and screenwriter best known for his work on spoof film franchises such as The Naked Gun and Police Academy.
-
B.
James Device
James Device was one of the accused witches in the 1612 Pendle witch trials in Lancashire, England, a notorious early modern English witchcraft case.
-
C.
Steve Meretzky
Steve Meretzky is an American game designer best known for his influential and humorous text adventures at Infocom, including collaborations with Douglas Adams.
-
D.
Charles Techman
Charles Techman is an actor who appeared in the film "Synecdoche, New York."
-
E.
Gadget Man
Gadget Man is a British television series that humorously reviews and demonstrates consumer technology and innovative gadgets in everyday situations.
- 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: PC Joe Penhale Triple: [Doc Martin, hasCharacter, PC Joe Penhale]
Generated description
PC Joe Penhale is a bumbling yet well-meaning village police officer in the British television series "Doc Martin."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PC Joe Penhale Target entity description: PC Joe Penhale is a bumbling yet well-meaning village police officer in the British television series "Doc Martin."
-
A.
Pat Proft
Pat Proft is an American comedy writer and screenwriter best known for his work on spoof film franchises such as The Naked Gun and Police Academy.
-
B.
James Device
James Device was one of the accused witches in the 1612 Pendle witch trials in Lancashire, England, a notorious early modern English witchcraft case.
-
C.
Steve Meretzky
Steve Meretzky is an American game designer best known for his influential and humorous text adventures at Infocom, including collaborations with Douglas Adams.
-
D.
Charles Techman
Charles Techman is an actor who appeared in the film "Synecdoche, New York."
-
E.
Gadget Man
Gadget Man is a British television series that humorously reviews and demonstrates consumer technology and innovative gadgets in everyday situations.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd050108190a584543cb93943a4 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8a688a48190848eb7f065aba146 |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fef9cf76cc8190898ea1e648da18ed |
completed | May 9, 2026, 9:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefab2d4cc8190acaa4b6341224633 |
completed | May 9, 2026, 9:13 a.m. |
Created at: April 10, 2026, 3:16 a.m.