Triple
T9686144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagyerdő Park |
E234411
|
entity |
| Predicate | hasLocalName |
P6353
|
FINISHED |
| Object |
Nagyerdő
Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
|
E821442
|
NE FINISHED |
How this triple was built (4 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: Nagyerdő | Statement: [Nagyerdő Park, hasLocalName, Nagyerdő]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagyerdő Context triple: [Nagyerdő Park, hasLocalName, Nagyerdő]
-
A.
Kunhegyes
Kunhegyes is a small town in Jász-Nagykun-Szolnok County in central Hungary, known for its rural character and agricultural surroundings.
-
B.
Lővérek Hills
Lővérek Hills is a forested hilly area near Sopron in western Hungary, known for its hiking trails, lookout towers, and recreational opportunities.
-
C.
Gödöllő Hills
Gödöllő Hills is a hilly geographical region in central Hungary known for its rolling landscapes, forests, and proximity to Budapest.
-
D.
Kékes
Kékes is the highest peak in Hungary, known for its popular hiking trails and ski resort facilities.
-
E.
Gellért-hegy
Gellért-hegy is a prominent hill overlooking the Danube in Budapest, known for its panoramic city views, historic monuments, and the iconic Citadella fortress.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nagyerdő Triple: [Nagyerdő Park, hasLocalName, Nagyerdő]
Generated description
Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nagyerdő Target entity description: Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
-
A.
Kunhegyes
Kunhegyes is a small town in Jász-Nagykun-Szolnok County in central Hungary, known for its rural character and agricultural surroundings.
-
B.
Lővérek Hills
Lővérek Hills is a forested hilly area near Sopron in western Hungary, known for its hiking trails, lookout towers, and recreational opportunities.
-
C.
Gödöllő Hills
Gödöllő Hills is a hilly geographical region in central Hungary known for its rolling landscapes, forests, and proximity to Budapest.
-
D.
Kékes
Kékes is the highest peak in Hungary, known for its popular hiking trails and ski resort facilities.
-
E.
Gellért-hegy
Gellért-hegy is a prominent hill overlooking the Danube in Budapest, known for its panoramic city views, historic monuments, and the iconic Citadella fortress.
- F. None of above. chosen
Provenance (5 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_69ca84ca73208190957a900c8543bdcc |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9cd2dab481908e0d3fed28de9d40 |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c40329448190bccac60a20dcb9d1 |
completed | April 5, 2026, 2:08 a.m. |
| NEDg | Description generation | batch_69d1c4eb7a0481908bbd72f6d28d4746 |
completed | April 5, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1c5c0e6e88190bbf6eb379e6d1aa3 |
completed | April 5, 2026, 2:15 a.m. |
Created at: March 30, 2026, 8:16 p.m.