Triple
T6429314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magda Elizabeth Kemeny |
E128139
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Magda |
E200104
|
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: Magda | Statement: [Magda Elizabeth Kemeny, givenName, Magda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magda Context triple: [Magda Elizabeth Kemeny, givenName, Magda]
-
A.
Magda
chosen
Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
-
B.
Marta
Marta is a legendary Brazilian footballer widely regarded as one of the greatest women’s players of all time.
-
C.
Marta
Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
-
D.
Marta
Marta is a small Italian town in the Lazio region, situated on the southern shore of Lake Bolsena and known for its lakeside scenery and historic center.
-
E.
Dagmara
Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
- 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_69c00838de888190af2eec0b80495efa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06923b12081908a09543450b88c24 |
completed | March 22, 2026, 10:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bbc865c81909bf064b9253bc263 |
completed | March 27, 2026, 9:19 a.m. |
Created at: March 22, 2026, 4:44 p.m.