Triple
T4861442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nan A. Talese |
E108668
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nan |
E409214
|
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: Nan | Statement: [Nan A. Talese, givenName, Nan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nan Context triple: [Nan A. Talese, givenName, Nan]
-
A.
Nan
chosen
Nan is a spirited, independent young woman in Louisa May Alcott’s novel "Jo’s Boys," known for challenging traditional gender roles and pursuing a medical career.
-
B.
Nanon
Nanon is a loyal and selfless servant in Honoré de Balzac’s novel "Eugénie Grandet," known for her devotion to the Grandet household and especially to Eugénie.
-
C.
Nanuya Levu
Nanuya Levu is a small, tropical Fijian island in the Yasawa archipelago, known for its secluded beaches and use as a filming location for the movie "The Blue Lagoon."
-
D.
Nain
Nain is a remote coastal town in northern Labrador, Canada, known as the administrative center of the Inuit region of Nunatsiavut.
-
E.
Nesta
Nesta is the middle name of legendary Jamaican reggae musician and cultural icon Bob Marley.
- 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d5f62b48190b367ed1b850cfbcb |
completed | March 20, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5cf921cc8190a092bb69c1981890 |
completed | March 21, 2026, 8:55 a.m. |
Created at: March 20, 2026, 1:26 p.m.