Triple
T16635350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eat a Bowl of Tea |
E404183
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Maysie Hoy |
—
|
NE NERFINISHED |
How this triple was built (2 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: Maysie Hoy | Statement: [Eat a Bowl of Tea, editor, Maysie Hoy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maysie Hoy Context triple: [Eat a Bowl of Tea, editor, Maysie Hoy]
-
A.
Maysie Hoy
chosen
Maysie Hoy is a Canadian film editor known for her work on numerous feature films, including collaborations with prominent directors such as Tyler Perry.
-
B.
Molly Blane
Molly Blane is a key member of the covert military team in the television series "The Unit," known for her resilience and role within the soldiers’ family network.
-
C.
Shirley Owens
Shirley Owens is an American singer best known as the lead vocalist of the girl group The Shirelles, pioneers of the early 1960s pop and R&B sound.
-
D.
Maxine Albro
Maxine Albro was an American muralist and painter associated with the New Deal era, best known for her vibrant frescoes and contributions to public art in San Francisco.
-
E.
Mary Wickes
Mary Wickes was an American character actress known for her sharp-tongued, comedic roles in film and television across several decades.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378e999d48190bff680040dbc883d |
completed | April 18, 2026, 12:28 p.m. |
Created at: April 10, 2026, 5:17 a.m.