Triple
T10085779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stefania |
E215216
|
entity |
| Predicate | hasMasculineForm |
P15475
|
FINISHED |
| Object | István |
E128847
|
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: István | Statement: [Stefania, hasMasculineForm, István]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: István Context triple: [Stefania, hasMasculineForm, István]
-
A.
István
chosen
István is the Hungarian given name of Stephen I of Hungary, the first Christian king and founder of the medieval Hungarian state.
-
B.
Lajos
Lajos is a Hungarian masculine given name commonly used in Central and Eastern Europe.
-
C.
László
László is a Hungarian given name most famously borne by the avant-garde artist and Bauhaus teacher László Moholy-Nagy.
-
D.
Imre
Imre is a Hungarian given name most famously borne by Imre Nagy, the reformist prime minister associated with the 1956 Hungarian Revolution.
-
E.
Miklós
Miklós is a Hungarian masculine given name, equivalent to Nicholas in English.
- 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_69ca83a1eed081908b2e9580f2ebeea7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd04609748190987a9364a387fa61 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d794653a948190899e60d18446213a |
completed | April 9, 2026, 11:58 a.m. |
Created at: March 30, 2026, 9 p.m.