Triple
T2650070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wanda Piłsudska |
E53875
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wanda |
E253732
|
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: Wanda | Statement: [Wanda Piłsudska, givenName, Wanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanda Context triple: [Wanda Piłsudska, givenName, Wanda]
-
A.
Wanda
chosen
Wanda is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
-
B.
Scarlet Witch
Scarlet Witch is a powerful Marvel Comics superhero and Avenger, known for her reality-warping chaos magic and complex moral journey.
-
C.
Margareta
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
-
D.
Miss Quill
Miss Quill is a sharp-tongued, battle-hardened alien freedom fighter and teacher from the Doctor Who spin-off series "Class."
-
E.
Liliana
Liliana is a feminine given name, often considered a more elaborate or romantic variant of Lily, used in various cultures around the world.
- 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd92f5f508190b4ca396c3f399e93 |
completed | March 7, 2026, 7:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98cc2d9881908556b915a1870c02 |
completed | March 10, 2026, 4:06 a.m. |
Created at: March 6, 2026, 9:53 p.m.