Triple
T15192996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rough Francis |
E363066
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Dan Davine
Dan Davine is a musician best known as a member of the punk rock band Rough Francis.
|
E1142121
|
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: Dan Davine | Statement: [Rough Francis, hasMember, Dan Davine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Davine Context triple: [Rough Francis, hasMember, Dan Davine]
-
A.
Larkin Seiple
Larkin Seiple is an American cinematographer known for his visually inventive work on films such as "Everything Everywhere All at Once."
-
B.
Robert Hillenbrand
Robert Hillenbrand is a distinguished British art historian renowned for his scholarship on Islamic art and architecture.
-
C.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
D.
Des Bishop
Des Bishop is an Irish-American stand-up comedian and television personality known for his socially conscious humor and work on Irish and Chinese culture.
-
E.
Danny Donahue
Danny Donahue is a central character in the comedy film "Role Models," serving as one of the key figures around whom the story’s mentorship and personal growth themes revolve.
- 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: Dan Davine Triple: [Rough Francis, hasMember, Dan Davine]
Generated description
Dan Davine is a musician best known as a member of the punk rock band Rough Francis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Davine Target entity description: Dan Davine is a musician best known as a member of the punk rock band Rough Francis.
-
A.
Larkin Seiple
Larkin Seiple is an American cinematographer known for his visually inventive work on films such as "Everything Everywhere All at Once."
-
B.
Robert Hillenbrand
Robert Hillenbrand is a distinguished British art historian renowned for his scholarship on Islamic art and architecture.
-
C.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
D.
Des Bishop
Des Bishop is an Irish-American stand-up comedian and television personality known for his socially conscious humor and work on Irish and Chinese culture.
-
E.
Danny Donahue
Danny Donahue is a central character in the comedy film "Role Models," serving as one of the key figures around whom the story’s mentorship and personal growth themes revolve.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067eb710819085211fd05d5fa5f0 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec8995bb08190bd7f0be0a0fcf1e7 |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69feca98251081909c07f7ba4a863ad8 |
completed | May 9, 2026, 5:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fecb1cc7d08190a77e8a444334b688 |
completed | May 9, 2026, 5:50 a.m. |
Created at: April 10, 2026, 3:10 a.m.