Triple
T22035008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zona Gale |
E544181
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Miss Lulu Bett |
—
|
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: Miss Lulu Bett | Statement: [Zona Gale, notableWork, Miss Lulu Bett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miss Lulu Bett Context triple: [Zona Gale, notableWork, Miss Lulu Bett]
-
A.
Miss Lulu Bett
chosen
"Miss Lulu Bett" is a 1920 novel by Zona Gale, notable for its realistic portrayal of a small-town woman's struggle for independence and its Pulitzer Prize–winning stage adaptation.
-
B.
Lulu
Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
-
C.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
D.
Lulu
Lulu is a Scottish singer and actress best known for her powerful pop vocals and hits like "To Sir with Love" and "Shout."
-
E.
Lulu
Lulu is a fictional character best known from the Japanese film "Swallowtail Butterfly," in which she is portrayed by actress Ayumi Ito.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f0594881909caf4fbc3e0a2d50 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:25 p.m.