Triple
T20592946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margit Saad |
E505976
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Margit |
—
|
NE NERFINISHED |
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: Margit | Statement: [Margit Saad, givenName, Margit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margit Context triple: [Margit Saad, givenName, Margit]
-
A.
Margit
chosen
Margit is a feminine given name used in various European countries, often considered a form of Margaret.
-
B.
Margit körút
Margit körút is a major boulevard in Budapest, Hungary, known for connecting the Buda side’s central districts and serving as an important traffic and public transport artery near the Danube.
-
C.
Märtha
Märtha was a Swedish princess and Crown Princess of Norway, known for her humanitarian work and influential role during World War II.
-
D.
Gretel
Gretel is a German feminine given name best known from the fairy tale "Hansel and Gretel," where it is used as the name of the young girl protagonist.
-
E.
Liesl
Liesl is a feminine given name, commonly used as a diminutive of names like Elisabeth in German-speaking regions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a97d63cc8190853e052d5930470d |
completed | April 20, 2026, 10:32 p.m. |
Created at: April 16, 2026, 11:40 a.m.