Triple
T7606104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danny Tripp |
E180107
|
entity |
| Predicate | worksWith |
P398
|
FINISHED |
| Object |
Cal Shanley
Cal Shanley is a television producer and production professional known for his behind-the-scenes work on American TV series.
|
E676766
|
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: Cal Shanley | Statement: [Danny Tripp, worksWith, Cal Shanley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cal Shanley Context triple: [Danny Tripp, worksWith, Cal Shanley]
-
A.
Cherry Starr
Cherry Starr is the widow of Hall of Fame Green Bay Packers quarterback Bart Starr and a longtime philanthropist known for her charitable and community work.
-
B.
Cheryl Malone
Cheryl Malone is a notable individual recognized for achievements significant enough to be associated with the Malone surname.
-
C.
Michele Hollister
Michele Hollister is a film editor known for her work on the 1999 drama film "Sunshine."
-
D.
Shirley Feeney
Shirley Feeney is a cheerful, optimistic Milwaukee brewery worker and one of the two titular roommates in the classic American sitcom "Laverne & Shirley."
-
E.
Joan Shawlee
Joan Shawlee was an American character actress best known for her comedic supporting roles in mid-20th-century films 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: Cal Shanley Triple: [Danny Tripp, worksWith, Cal Shanley]
Generated description
Cal Shanley is a television producer and production professional known for his behind-the-scenes work on American TV series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cal Shanley Target entity description: Cal Shanley is a television producer and production professional known for his behind-the-scenes work on American TV series.
-
A.
Cherry Starr
Cherry Starr is the widow of Hall of Fame Green Bay Packers quarterback Bart Starr and a longtime philanthropist known for her charitable and community work.
-
B.
Cheryl Malone
Cheryl Malone is a notable individual recognized for achievements significant enough to be associated with the Malone surname.
-
C.
Michele Hollister
Michele Hollister is a film editor known for her work on the 1999 drama film "Sunshine."
-
D.
Shirley Feeney
Shirley Feeney is a cheerful, optimistic Milwaukee brewery worker and one of the two titular roommates in the classic American sitcom "Laverne & Shirley."
-
E.
Joan Shawlee
Joan Shawlee was an American character actress best known for her comedic supporting roles in mid-20th-century films 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fcfcfc8190a29a0b5cd3e8927a |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c86857db14819086d5ebd825d30e77 |
completed | March 28, 2026, 11:46 p.m. |
| NEDg | Description generation | batch_69c86a12e1f08190ab214f4e95e986db |
completed | March 28, 2026, 11:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c86a5b6f188190aafbf2e9fcb8b972 |
completed | March 28, 2026, 11:55 p.m. |
Created at: March 27, 2026, 3:54 p.m.