Triple
T3234640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Rougon-Macquart |
E67822
|
entity |
| Predicate | notableWorkInCycle |
P24127
|
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: [Les Rougon-Macquart, notableWorkInCycle, Nana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nana Context triple: [Les Rougon-Macquart, notableWorkInCycle, 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 popular hit single by Tanzanian Bongo Flava artist Diamond Platnumz, known for its romantic theme and widespread success across East Africa.
-
C.
Nani and Nana
"Nani and Nana" is a well-known chutney music song recognized for its catchy rhythm and popularity within Indo-Caribbean musical culture.
-
D.
Nene
Nene was the principal wife of Japanese warlord Toyotomi Hideyoshi and a politically influential noblewoman during the late Sengoku period.
-
E.
Nena
Nena is a German pop singer and actress best known internationally for her 1983 hit song "99 Luftballons."
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaede0bdc8190a466f11bf2c50836 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28ea696a08190a17cbeeef7632977 |
completed | March 12, 2026, 10 a.m. |
Created at: March 8, 2026, 3:08 p.m.