Triple
T2024988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miklós Horthy |
E44186
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Kenderes |
E225331
|
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: Kenderes | Statement: [Miklós Horthy, residence, Kenderes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenderes Context triple: [Miklós Horthy, residence, Kenderes]
-
A.
Kenderes
chosen
Kenderes is a town in Hungary best known as the birthplace and family estate center of Regent Miklós Horthy.
-
B.
Hasköy
Hasköy is a historic neighborhood on the European side of Istanbul, known for its multicultural past and its location along the Golden Horn.
-
C.
Kameçvara
Kameçvara was a prominent king of the medieval Javanese Kediri Kingdom, remembered for his prosperous reign and association with the classic romance tale of Panji.
-
D.
Bad Kösen
Bad Kösen is a spa town in the German state of Saxony-Anhalt, known for its saline springs, historic graduation towers, and scenic location along the Saale River.
-
E.
Demerdzhi
Demerdzhi is a notable mountain massif in Crimea, famous for its striking rock formations and scenic landscapes.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8f2cd5c8190b19da6f6aa2001d6 |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fead86c8190b3b247e88aad30f9 |
completed | March 9, 2026, 1:18 a.m. |
Created at: March 4, 2026, 7:38 p.m.