Triple
T16833748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nan |
E409214
|
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, givenName, Nan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nan Context triple: [Nan, 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.
Nan
Nan is a small historic city in northern Thailand known for its tranquil atmosphere, traditional Lanna culture, and ornate Buddhist temples.
-
C.
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.
-
D.
Nane
Nane is a Swedish lawyer and artist best known as the widow of former United Nations Secretary-General Kofi Annan.
-
E.
Naqu
Naqu is a high-altitude city and prefecture-level administrative region in northern Tibet, China, known for its vast grasslands and harsh climate.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b31981ac8190bbd9720efe842778 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b2a4101081908faa1b85d338b05e |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.