Triple
T14800439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nana (1926 film) |
E347894
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Nana |
E70011
|
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: Nana | Statement: [Nana (1926 film), title, Nana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nana Context triple: [Nana (1926 film), title, Nana]
-
A.
Nana
chosen
Nana is an 1880 naturalist novel by Émile Zola that follows the rise and fall of a Parisian courtesan as a critique of Second Empire society.
-
B.
Nana
Nana is a figure in Phrygian mythology, often regarded as the mother of the vegetation god Attis and associated with themes of miraculous birth and fertility.
-
C.
Nana
Nana is an Indian honorific title commonly used to respectfully address an elder man or grandfather.
-
D.
Nana
Nana is a person or character associated with Louiset, likely within a shared narrative, social, or creative context.
-
E.
Nana
"Nana" is a popular hit single by Tanzanian Bongo Flava artist Diamond Platnumz, known for its romantic theme and widespread success across East Africa.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd62c36c81909c2993dc7d1a79ea |
completed | April 14, 2026, 11:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feadfd53a48190b7efaa4c470da784 |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 1:31 a.m.