Triple
T15008790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franklin & Bash |
E377780
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Peter Bash
Peter Bash is a charismatic, risk-taking defense attorney and one of the two unconventional lawyer protagonists in the television series "Franklin & Bash."
|
E1132196
|
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: Peter Bash | Statement: [Franklin & Bash, character, Peter Bash]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Bash Context triple: [Franklin & Bash, character, Peter Bash]
-
A.
Peter Pau
Peter Pau is an acclaimed Hong Kong cinematographer best known internationally for his Oscar-winning work on the film "Crouching Tiger, Hidden Dragon."
-
B.
Adam Bulgasem
Adam Bulgasem is a musician known for being a member of the experimental rock band Black Mountain.
-
C.
Peter Nashel
Peter Nashel is an American composer known for his film and television scores, including his work on the darkly comedic biopic "I, Tonya."
-
D.
Peter Russo
Peter Russo is a troubled Pennsylvania congressman and key character in the political drama series "House of Cards."
-
E.
Peter Cambor
Peter Cambor is an American actor best known for his television roles, including a main part on the series "NCIS: Los Angeles" and a starring role in the comedy-drama "Roadies."
- 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: Peter Bash Triple: [Franklin & Bash, character, Peter Bash]
Generated description
Peter Bash is a charismatic, risk-taking defense attorney and one of the two unconventional lawyer protagonists in the television series "Franklin & Bash."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Bash Target entity description: Peter Bash is a charismatic, risk-taking defense attorney and one of the two unconventional lawyer protagonists in the television series "Franklin & Bash."
-
A.
Peter Pau
Peter Pau is an acclaimed Hong Kong cinematographer best known internationally for his Oscar-winning work on the film "Crouching Tiger, Hidden Dragon."
-
B.
Adam Bulgasem
Adam Bulgasem is a musician known for being a member of the experimental rock band Black Mountain.
-
C.
Peter Nashel
Peter Nashel is an American composer known for his film and television scores, including his work on the darkly comedic biopic "I, Tonya."
-
D.
Peter Russo
Peter Russo is a troubled Pennsylvania congressman and key character in the political drama series "House of Cards."
-
E.
Peter Cambor
Peter Cambor is an American actor best known for his television roles, including a main part on the series "NCIS: Los Angeles" and a starring role in the comedy-drama "Roadies."
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded73348d4819091d9e7f1b0fed822 |
completed | April 15, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe96a52bb08190961e3f18d751fe2a |
completed | May 9, 2026, 2:06 a.m. |
| NEDg | Description generation | batch_69fe98bf505c819089740180a763db34 |
completed | May 9, 2026, 2:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9aab47888190812ff9732380e124 |
completed | May 9, 2026, 2:23 a.m. |
Created at: April 10, 2026, 2:55 a.m.