Triple
T9911887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nimue |
E185162
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Viviane |
E741555
|
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: Viviane | Statement: [Nimue, alsoKnownAs, Viviane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viviane Context triple: [Nimue, alsoKnownAs, Viviane]
-
A.
Viviane
chosen
Viviane is a legendary enchantress of Arthurian romance, often identified as the Lady of the Lake and known for her role in mentoring and imprisoning the wizard Merlin.
-
B.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
C.
Alessandra
Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
-
D.
Liliane
Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb5391b8081908094b88cdde4b55a |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d22c56081909c3e4b8ca0e81fe7 |
completed | April 5, 2026, 10:44 a.m. |
Created at: March 30, 2026, 8:41 p.m.