Triple
T13758939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waite |
E330548
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Stacey Waite
Stacey Waite is an American poet, educator, and scholar known for work exploring gender, queerness, and pedagogy.
|
E1059238
|
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: Stacey Waite | Statement: [Waite, hasNotableBearer, Stacey Waite]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stacey Waite Context triple: [Waite, hasNotableBearer, Stacey Waite]
-
A.
Linda Steele
Linda Steele is known as the wife of British speculative fiction author Michael Moorcock.
-
B.
Lisa Vidal
Lisa Vidal is an American actress known for her work in television dramas and films, including prominent roles in series like "The Division," "ER," and "Being Mary Jane."
-
C.
Rachel Roberts
Rachel Roberts is a Canadian model and actress known for her work in fashion campaigns and films such as "Simone" and "Entourage."
-
D.
Rachel Roberts
Rachel Roberts was a Welsh actress known for her intense, emotionally powerful performances in British and international films during the mid-20th century.
-
E.
Peggy Stephenson
Peggy Stephenson is the sympathetic and resilient young woman Teresa Wright portrays in the classic post–World War II film "The Best Years of Our Lives."
- 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: Stacey Waite Triple: [Waite, hasNotableBearer, Stacey Waite]
Generated description
Stacey Waite is an American poet, educator, and scholar known for work exploring gender, queerness, and pedagogy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stacey Waite Target entity description: Stacey Waite is an American poet, educator, and scholar known for work exploring gender, queerness, and pedagogy.
-
A.
Linda Steele
Linda Steele is known as the wife of British speculative fiction author Michael Moorcock.
-
B.
Lisa Vidal
Lisa Vidal is an American actress known for her work in television dramas and films, including prominent roles in series like "The Division," "ER," and "Being Mary Jane."
-
C.
Rachel Roberts
Rachel Roberts is a Canadian model and actress known for her work in fashion campaigns and films such as "Simone" and "Entourage."
-
D.
Rachel Roberts
Rachel Roberts was a Welsh actress known for her intense, emotionally powerful performances in British and international films during the mid-20th century.
-
E.
Peggy Stephenson
Peggy Stephenson is the sympathetic and resilient young woman Teresa Wright portrays in the classic post–World War II film "The Best Years of Our Lives."
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0223ab9081909db05334860405e0 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a85dfa6881908da90886db4aa1bb |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a994cd688190a077a4854c5c71c9 |
completed | May 3, 2026, 8:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7aa32b8c8819088bbc9e478c21c06 |
completed | May 3, 2026, 8:04 p.m. |
Created at: April 9, 2026, 10:09 p.m.