Triple
T15315553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc Martin |
E366146
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Bert Large
Bert Large is a comically enterprising plumber and handyman in the British television series "Doc Martin," known for his get-rich-quick schemes and larger-than-life personality.
|
E1149674
|
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: Bert Large | Statement: [Doc Martin, hasCharacter, Bert Large]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bert Large Context triple: [Doc Martin, hasCharacter, Bert Large]
-
A.
Bert
Bert is a Swedish film or television production best known as an early directorial work by acclaimed filmmaker Tomas Alfredson.
-
B.
Bert
Bert is the given name of Bert Hölldobler, a renowned German behavioral biologist and sociobiologist known for his pioneering research on ants and social insects.
-
C.
Bert
Bert is a film director best known for co-directing the 2019 coming-of-age comedy-drama "Troop Zero."
-
D.
Bert
Bert is the commonly used short form of the Dutch politician and diplomat Bert Koenders’ given name.
-
E.
Bert
Bert is a masculine given name, often used as a short form of names like Albert, Herbert, or Bertram.
- 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: Bert Large Triple: [Doc Martin, hasCharacter, Bert Large]
Generated description
Bert Large is a comically enterprising plumber and handyman in the British television series "Doc Martin," known for his get-rich-quick schemes and larger-than-life personality.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bert Large Target entity description: Bert Large is a comically enterprising plumber and handyman in the British television series "Doc Martin," known for his get-rich-quick schemes and larger-than-life personality.
-
A.
Bert
Bert is a Swedish film or television production best known as an early directorial work by acclaimed filmmaker Tomas Alfredson.
-
B.
Bert
Bert is a film director best known for co-directing the 2019 coming-of-age comedy-drama "Troop Zero."
-
C.
Bert
Bert is a serious, detail-oriented Muppet from Sesame Street, best known for his love of pigeons, paper clips, and his comedic odd-couple friendship with Ernie.
-
D.
Bert
Bert is the commonly used short form of the Dutch politician and diplomat Bert Koenders’ given name.
-
E.
Bert
Bert is the nickname of Bert Hinkler, an Australian aviation pioneer and record-breaking solo long-distance pilot of the early 20th century.
- 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.