Triple
T6527380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zosia Mamet |
E151339
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Zosia |
E423642
|
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: Zosia | Statement: [Zosia Mamet, givenName, Zosia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zosia Context triple: [Zosia Mamet, givenName, Zosia]
-
A.
Zofia
chosen
Zofia is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
-
B.
Dagmara
Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
-
C.
Sylwia
Sylwia is a feminine given name, primarily used in Poland, that is a cognate of the name Sylvia.
-
D.
Roksana
Roksana is a feminine given name, commonly used in various Slavic and Persian-influenced cultures, that is a variant of the name Roxana.
-
E.
Michalina
Michalina is a feminine given name of Slavic origin, commonly used in Polish-speaking countries.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ada8a0e48190947616f3a09a2cba |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d52a642c8190a50988f3faf61d39 |
completed | March 27, 2026, 7:06 p.m. |
Created at: March 27, 2026, 1:45 p.m.