Triple
T4637189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilella Cather |
E101560
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Wilella |
E101560
|
NE FINISHED |
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: Wilella | Statement: [Wilella Cather, hasGivenName, Wilella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilella Context triple: [Wilella Cather, hasGivenName, Wilella]
-
A.
Wilella
chosen
Wilella is the full given name of American novelist Willa Cather, renowned for her works depicting frontier life on the Great Plains.
-
B.
Sheilia
Sheilia is a feminine given name, typically considered an alternative spelling of the name Sheila.
-
C.
Marzelline
Marzelline is a character in Beethoven's opera "Fidelio," portrayed as the jailer Rocco’s daughter who becomes romantically entangled with the disguised heroine.
-
D.
Lela
Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
-
E.
Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43d2f1c081908cd4b7ec48ecc73d |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a62a9e48190b0cf1cbcc51f00c0 |
completed | March 20, 2026, 2:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be036d7aa081908b4b361dbae8ebc7 |
completed | March 21, 2026, 2:33 a.m. |
Created at: March 20, 2026, 1:13 p.m.