Triple
T14148228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zala György |
E350607
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Zala György |
E350607
|
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: Zala György | Statement: [Zala György, name, Zala György]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zala György Context triple: [Zala György, name, Zala György]
-
A.
Zala György
chosen
Zala György was a Hungarian sculptor best known for his monumental public statues and memorials in Budapest at the turn of the 20th century.
-
B.
Miklós Nagy
Miklós Nagy is a personal name borne by several notable individuals, typically of Hungarian origin.
-
C.
Gyula Halász
Gyula Halász, better known by his pseudonym Brassaï, was a Hungarian–French photographer famed for his evocative black-and-white images of Parisian nightlife in the 1930s.
-
D.
József Vágó
József Vágó was a Hungarian architect associated with early 20th-century modernist and Art Nouveau movements, known for his contributions to significant international projects.
-
E.
Vilmos Gábor
Vilmos Gábor was the father of Hungarian-American actress and socialite Zsa Zsa Gabor.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61237ef481909374c1f68a2370b7 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19395924819084d78815c3f12f43 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 12:55 a.m.