Triple
T5987142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Stroman |
E133255
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Stroman
Stroman is a surname most prominently associated with Susan Stroman, the acclaimed American theatre director and choreographer known for her work on Broadway musicals.
|
E559216
|
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: Stroman | Statement: [Susan Stroman, familyName, Stroman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stroman Context triple: [Susan Stroman, familyName, Stroman]
-
A.
Strom
Strom is the given name of Strom Thurmond, a long-serving U.S. senator from South Carolina known for his staunch segregationist views and record-breaking tenure in Congress.
-
B.
Stellan
Stellan is a masculine given name of Scandinavian origin, most notably borne by Swedish actor Stellan Skarsgård.
-
C.
Arvin
Arvin is a small agricultural city in Southern California’s San Joaquin Valley, known for its farming economy and diverse rural community.
-
D.
Sedov
Sedov is a Russian surname most notably associated with Lev Sedov, the revolutionary son of Leon Trotsky.
-
E.
Bomer
Bomer is the surname of American actor Matt Bomer, known for his roles in television and film such as "White Collar" and "The Normal Heart."
- 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: Stroman Triple: [Susan Stroman, familyName, Stroman]
Generated description
Stroman is a surname most prominently associated with Susan Stroman, the acclaimed American theatre director and choreographer known for her work on Broadway musicals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stroman Target entity description: Stroman is a surname most prominently associated with Susan Stroman, the acclaimed American theatre director and choreographer known for her work on Broadway musicals.
-
A.
Strom
Strom is the given name of Strom Thurmond, a long-serving U.S. senator from South Carolina known for his staunch segregationist views and record-breaking tenure in Congress.
-
B.
Stellan
Stellan is a masculine given name of Scandinavian origin, most notably borne by Swedish actor Stellan Skarsgård.
-
C.
Arvin
Arvin is a small agricultural city in Southern California’s San Joaquin Valley, known for its farming economy and diverse rural community.
-
D.
Sedov
Sedov is a Russian surname most notably associated with Lev Sedov, the revolutionary son of Leon Trotsky.
-
E.
Bomer
Bomer is the surname of American actor Matt Bomer, known for his roles in television and film such as "White Collar" and "The Normal Heart."
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc38a308190a368c5c787a5fc64 |
completed | March 22, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e42c16fc8190821a6f391ad3109b |
completed | March 23, 2026, 6:56 a.m. |
| NEDg | Description generation | batch_69c0f82a22448190b7f7209af49ca59c |
completed | March 23, 2026, 8:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0fa95a1308190914a81049c0aac1e |
completed | March 23, 2026, 8:32 a.m. |
Created at: March 22, 2026, 4:04 p.m.