Triple
T18968199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clockwise |
E464096
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Sharon Maiden
Sharon Maiden is an actress known for her role in the British film "Clockwise" alongside John Cleese.
|
E1352663
|
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: Sharon Maiden | Statement: [Clockwise, starring, Sharon Maiden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharon Maiden Context triple: [Clockwise, starring, Sharon Maiden]
-
A.
Sharon
Sharon is a fictional character from Frederick Buechner’s comic novel sequence *The Book of Bebb*, which follows the eccentric evangelist Leo Bebb and those in his orbit.
-
B.
Sharon
Sharon is a small community within the town of East Gwillimbury in Ontario, Canada, known for its residential character and local heritage.
-
C.
Sharon
Sharon is a small unincorporated community located in the state of Mississippi in the United States.
-
D.
Sharon
Sharon is the given first name of American actress S. Epatha Merkerson, best known for her long-running role as Lieutenant Anita Van Buren on the television series "Law & Order."
-
E.
Sharon
Sharon is a suburban town in Norfolk County, Massachusetts, known for its residential character, natural conservation areas, and proximity to Boston.
- 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: Sharon Maiden Triple: [Clockwise, starring, Sharon Maiden]
Generated description
Sharon Maiden is an actress known for her role in the British film "Clockwise" alongside John Cleese.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sharon Maiden Target entity description: Sharon Maiden is an actress known for her role in the British film "Clockwise" alongside John Cleese.
-
A.
Sharon
Sharon is a fictional character from Frederick Buechner’s comic novel sequence *The Book of Bebb*, which follows the eccentric evangelist Leo Bebb and those in his orbit.
-
B.
Sharon
Sharon is a small community within the town of East Gwillimbury in Ontario, Canada, known for its residential character and local heritage.
-
C.
Sharon
Sharon is a small unincorporated community located in the state of Mississippi in the United States.
-
D.
Sharon
Sharon is the given first name of American actress S. Epatha Merkerson, best known for her long-running role as Lieutenant Anita Van Buren on the television series "Law & Order."
-
E.
Sharon
Sharon is a suburban town in Norfolk County, Massachusetts, known for its residential character, natural conservation areas, and proximity to Boston.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6172888819090a8b3cb1db20496 |
completed | April 20, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05ad37581c8190ac75d3e932e08195 |
completed | May 14, 2026, 11:08 a.m. |
| NEDg | Description generation | batch_6a05aeedfd248190a69d128a1ba011e4 |
completed | May 14, 2026, 11:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05af7a2c408190b47f2f253bf8c862 |
completed | May 14, 2026, 11:18 a.m. |
Created at: April 10, 2026, noon