Triple
T5664659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Foe |
E124828
|
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 Foe, notableWork, Roxana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roxana Context triple: [Daniel Foe, 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.
Isidora
Isidora is a feminine given name of Greek origin, commonly considered the female form of Isidore and meaning "gift of Isis."
-
C.
Leonora
Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
-
D.
Leonora
Leonora is a remote mining town in Western Australia’s Goldfields-Esperance region, historically significant for its goldfields and outback heritage.
-
E.
Leonessa
Leonessa is a historic mountain town in central Italy, known for its medieval architecture and scenic location in the Apennines.
- 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_69c00828906881908966f270b8f130cf |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0232497a08190ab7227f0e135a29e |
completed | March 22, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04dad0af4819088280f2d97173e9e |
completed | March 22, 2026, 8:14 p.m. |
Created at: March 22, 2026, 3:43 p.m.