Triple
T2872995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magda Elizabeth Polanyi |
E56808
|
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 Polanyi, givenName, Magda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magda Context triple: [Magda Elizabeth Polanyi, 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.
Margareta
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
-
E.
Beata
Beata is a feminine given name of Latin origin, commonly used in various European countries and meaning "blessed" or "happy."
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abdfe59ef88190b8bdfdd03e8965f3 |
completed | March 7, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01db40e388190a208fe58e2ed6029 |
completed | March 10, 2026, 1:33 p.m. |
Created at: March 6, 2026, 10:03 p.m.