Triple
T8478085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mecsek Mountains |
E200445
|
entity |
| Predicate | hasPeak |
P8205
|
FINISHED |
| Object | Zengő |
E735631
|
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: Zengő | Statement: [Mecsek Mountains, hasPeak, Zengő]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zengő Context triple: [Mecsek Mountains, hasPeak, Zengő]
-
A.
Zengő
chosen
Zengő is a prominent peak in southern Hungary known for its scenic hiking trails and panoramic views over the Mecsek mountain range.
-
B.
Zagyva
Zagyva is a river in northern Hungary that flows through towns such as Salgótarján and Hatvan before joining the Tisza River.
-
C.
Enying
Enying is a small town in central Hungary known for its agricultural surroundings and location within Fejér County.
-
D.
Somlyó
Somlyó is a historical locality in the Kingdom of Hungary, best known as the birthplace of Stephen Báthory, who became King of Poland and Grand Duke of Lithuania in the 16th century.
-
E.
Losonczi
Losonczi is a Hungarian surname most notably borne by Pál Losonczi, a former Chairman of the Presidential Council of the Hungarian People's Republic.
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5216d6481908e25a49bcc2e00cc |
completed | March 31, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4de95e3081908277c65598f3884a |
completed | April 2, 2026, 11:07 a.m. |
Created at: March 30, 2026, 6:12 p.m.