Triple
T17548777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sherry Lansing |
E427397
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sherry |
—
|
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: Sherry | Statement: [Sherry Lansing, givenName, Sherry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sherry Context triple: [Sherry Lansing, givenName, Sherry]
-
A.
Sherry
chosen
Sherry is a feminine given name commonly used in English-speaking countries, often associated with names like Cheryl or derived from the fortified wine sherry.
-
B.
Sherry
Sherry is a surname most notably associated with William Grant Sherry, an American painter and the third husband of actress Bette Davis.
-
C.
Kina Lillet
Kina Lillet was a French aromatized wine-based aperitif, flavored with quinine and citrus, historically used in classic cocktails such as the Vesper.
-
D.
Manzanilla sherry
Manzanilla sherry is a pale, dry style of fino sherry uniquely aged under flor yeast by the sea in Sanlúcar de Barrameda, giving it a distinctive light, salty character.
-
E.
Vino
Vino is a VNC-compatible remote desktop server for the GNOME desktop environment on Unix-like systems.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e454631e148190a1d46a6ecb79c71a |
completed | April 19, 2026, 4:04 a.m. |
Created at: April 10, 2026, 5:49 a.m.