Triple
T1078501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Defoe |
E23892
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Roxana |
E66871
|
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: Roxana | Statement: [Daniel Defoe, notableWork, Roxana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roxana Context triple: [Daniel Defoe, notableWork, Roxana]
-
A.
Roxana
chosen
Roxana is a feminine given name of Persian origin, historically associated with figures such as the wife of Alexander the Great and later borne by various notable women.
-
B.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
C.
Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
-
D.
Clementina
Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
-
E.
Rosaura
Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b943b41481909b24050ca7e78971 |
completed | March 1, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5ea491ec8190bf6bd84ecb5af341 |
completed | March 7, 2026, 5:21 p.m. |
Created at: March 1, 2026, 7:42 p.m.