Triple
T22648746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanci Caroline Griffith |
E559038
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nanci |
—
|
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: Nanci | Statement: [Nanci Caroline Griffith, givenName, Nanci]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanci Context triple: [Nanci Caroline Griffith, givenName, Nanci]
-
A.
Nanci
chosen
Nanci is a feminine given name most notably associated with the late American folk and country singer-songwriter Nanci Griffith.
-
B.
Nancy Marchand
Nancy Marchand was an acclaimed American actress best known for her roles on the television series "Lou Grant" and "The Sopranos."
-
C.
Nanci Dakesian
Nanci Dakesian is a comic book editor known for her work on Marvel titles, including the 1998 Inhumans series.
-
D.
Nina Holiday
Nina Holiday is a character portrayed by Idara Victor, best known as a tech-savvy analyst on the television series "Rizzoli & Isles."
-
E.
Queen Nancy
Queen Nancy is the royal title assumed by Nancy Tremaine, a character from Disney’s Enchanted series who becomes queen in the fairy-tale kingdom of Andalasia.
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1703a84b081909a683f8c850dcbf9 |
completed | April 29, 2026, 2:43 a.m. |
Created at: April 17, 2026, 3:05 p.m.