Triple
T4577404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verna Fields |
E123170
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Verna |
E123170
|
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: Verna | Statement: [Verna Fields, givenName, Verna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verna Context triple: [Verna Fields, givenName, Verna]
-
A.
Verna
chosen
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
-
B.
Eudora
Eudora is a figure from Greek mythology known as one of the daughters of the Titan Atlas.
-
C.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
D.
Myra
Myra is a feminine given name used in various cultures, often associated with individuals of Jewish and English-speaking backgrounds.
-
E.
Myra
Myra was an ancient Greek city in Lycia, in what is now southwestern Turkey, historically notable as a major early Christian center and the bishopric of Saint Nicholas.
- 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_69bd46466c7081909d07f36be2d08804 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd58e153908190ac8f578e03aecdfc |
completed | March 20, 2026, 2:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3ee510481909d481b157bd0b2bd |
completed | March 20, 2026, 11:10 p.m. |
Created at: March 20, 2026, 1:10 p.m.