Triple
T21876526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Ladies Who Lunch |
E540157
|
entity |
| Predicate | theatricalRole |
P12691
|
FINISHED |
| Object | Joanne |
—
|
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: Joanne | Statement: [The Ladies Who Lunch, theatricalRole, Joanne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joanne Context triple: [The Ladies Who Lunch, theatricalRole, Joanne]
-
A.
Joanne
chosen
Joanne is a feminine given name of Hebrew origin, commonly used in English-speaking countries.
-
B.
Joanne
"Joanne" is a country- and rock-influenced studio album by American singer-songwriter Lady Gaga that explores themes of family, loss, and personal identity.
-
C.
Joanie
Joanie is a diminutive form of the given name Joan, often used as a familiar or affectionate nickname.
-
D.
Joanna
Joanna is a feminine given name used in various cultures, often associated with forms of the name John and shared by many notable historical and contemporary figures.
-
E.
Joanna
Joanna is a key character in the cult-classic comedy film "Office Space," known as the friendly waitress who becomes the love interest of the protagonist.
- 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_69e0c479a98081908ce333853fdd4348 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f33a4e608190bd25bfa3fb5cac10 |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 7:02 p.m.