Triple
T9969788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan in Real Life |
E196174
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Dan Burns
Dan Burns is the widowed advice columnist and devoted father at the center of the romantic comedy-drama film "Dan in Real Life."
|
E840250
|
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 Burns | Statement: [Dan in Real Life, mainCharacter, Dan Burns]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Burns Context triple: [Dan in Real Life, mainCharacter, Dan Burns]
-
A.
Dan Burden
Dan Burden is an American cycling and walkability advocate best known for co-founding the Adventure Cycling Association and promoting bicycle-friendly community design.
-
B.
Jonathan Oldbuck
Jonathan Oldbuck is a fictional, eccentric antiquary and amateur historian who serves as the central figure in Sir Walter Scott’s novel "The Antiquary."
-
C.
Jere Burns
Jere Burns is an American character actor known for his sharp, often villainous or darkly comedic roles in television series such as "Dear John," "Justified," and "Burn Notice."
-
D.
Dan Jinks
Dan Jinks is an American film and television producer best known for acclaimed movies such as "American Beauty" and "Big Fish."
-
E.
Ken Drake
Ken Drake was an American character actor known for his numerous supporting roles in mid-20th-century film and television.
- 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 Burns Triple: [Dan in Real Life, mainCharacter, Dan Burns]
Generated description
Dan Burns is the widowed advice columnist and devoted father at the center of the romantic comedy-drama film "Dan in Real Life."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Burns Target entity description: Dan Burns is the widowed advice columnist and devoted father at the center of the romantic comedy-drama film "Dan in Real Life."
-
A.
Dan Burden
Dan Burden is an American cycling and walkability advocate best known for co-founding the Adventure Cycling Association and promoting bicycle-friendly community design.
-
B.
Jonathan Oldbuck
Jonathan Oldbuck is a fictional, eccentric antiquary and amateur historian who serves as the central figure in Sir Walter Scott’s novel "The Antiquary."
-
C.
Jere Burns
Jere Burns is an American character actor known for his sharp, often villainous or darkly comedic roles in television series such as "Dear John," "Justified," and "Burn Notice."
-
D.
Dan Jinks
Dan Jinks is an American film and television producer best known for acclaimed movies such as "American Beauty" and "Big Fish."
-
E.
Ken Drake
Ken Drake was an American character actor known for his numerous supporting roles in mid-20th-century film and television.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b7ea9881908a56f11e2e446dd0 |
completed | April 2, 2026, 12:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b5d295908190a064fb72d65b6e24 |
completed | April 5, 2026, 7:19 p.m. |
| NEDg | Description generation | batch_69d2b741cad481909f04e2f8da68753c |
completed | April 5, 2026, 7:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2b805afa08190a43745d764a75050 |
completed | April 5, 2026, 7:29 p.m. |
Created at: March 30, 2026, 8:48 p.m.