Triple
T6523821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Stark |
E151251
|
entity |
| Predicate | partner |
P1136
|
FINISHED |
| Object |
Jack Bailey
Jack Bailey is a fictional law enforcement officer who serves as the straight-laced partner to the reckless detective Dan Stark in the TV series "The Good Guys."
|
E602097
|
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: Jack Bailey | Statement: [Dan Stark, partner, Jack Bailey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Bailey Context triple: [Dan Stark, partner, Jack Bailey]
-
A.
Sean Bailey
Sean Bailey is an American film producer and studio executive known for overseeing major projects at Walt Disney Studios and producing acclaimed films such as "Gone Baby Gone."
-
B.
Sam Baldwin
Sam Baldwin is the widowed architect and devoted father portrayed by Tom Hanks in the romantic comedy film "Sleepless in Seattle."
-
C.
Jeff Bailey
Jeff Bailey is the enigmatic private detective and central protagonist of the classic 1947 film noir "Out of the Past."
-
D.
Guy Bensley
Guy Bensley is a film editor best known for his work on the comedy spy film "Johnny English Reborn."
-
E.
Paul Ballard
Paul Ballard is a determined FBI agent in the television series "Dollhouse" who becomes obsessed with uncovering the truth behind the mysterious Dollhouse organization.
- 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: Jack Bailey Triple: [Dan Stark, partner, Jack Bailey]
Generated description
Jack Bailey is a fictional law enforcement officer who serves as the straight-laced partner to the reckless detective Dan Stark in the TV series "The Good Guys."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jack Bailey Target entity description: Jack Bailey is a fictional law enforcement officer who serves as the straight-laced partner to the reckless detective Dan Stark in the TV series "The Good Guys."
-
A.
Sean Bailey
Sean Bailey is an American film producer and studio executive known for overseeing major projects at Walt Disney Studios and producing acclaimed films such as "Gone Baby Gone."
-
B.
Sam Baldwin
Sam Baldwin is the widowed architect and devoted father portrayed by Tom Hanks in the romantic comedy film "Sleepless in Seattle."
-
C.
Jeff Bailey
Jeff Bailey is the enigmatic private detective and central protagonist of the classic 1947 film noir "Out of the Past."
-
D.
Guy Bensley
Guy Bensley is a film editor best known for his work on the comedy spy film "Johnny English Reborn."
-
E.
Paul Ballard
Paul Ballard is a determined FBI agent in the television series "Dollhouse" who becomes obsessed with uncovering the truth behind the mysterious Dollhouse organization.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad970afc81909d3231203eacf413 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb74dfac81908eb44811869450ae |
completed | March 27, 2026, 6:24 p.m. |
| NEDg | Description generation | batch_69c6cd049fac81908c955caa0ccac5ba |
completed | March 27, 2026, 6:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6ce00096c8190a3015bcd392e0ce4 |
completed | March 27, 2026, 6:35 p.m. |
Created at: March 27, 2026, 1:45 p.m.