Triple
T3216086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matra Mountains |
E67397
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Gyöngyös
Gyöngyös is a historic town in northern Hungary known as a gateway to the Mátra mountain range and its surrounding wine-producing region.
|
E339338
|
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: Gyöngyös | Statement: [Matra Mountains, hasSettlement, Gyöngyös]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyöngyös Context triple: [Matra Mountains, hasSettlement, Gyöngyös]
-
A.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
-
B.
Sátoraljaújhely
Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
-
C.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
D.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
E.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
- 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: Gyöngyös Triple: [Matra Mountains, hasSettlement, Gyöngyös]
Generated description
Gyöngyös is a historic town in northern Hungary known as a gateway to the Mátra mountain range and its surrounding wine-producing region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gyöngyös Target entity description: Gyöngyös is a historic town in northern Hungary known as a gateway to the Mátra mountain range and its surrounding wine-producing region.
-
A.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
-
B.
Sátoraljaújhely
Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
-
C.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
D.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
E.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
- 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_69ad858b8adc8190ad989712c87a476b |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adab096b588190b22e41a76263ae92 |
completed | March 8, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2771204e0819086ae2838a368589a |
completed | March 12, 2026, 8:19 a.m. |
| NEDg | Description generation | batch_69b27844c6708190ac61f00a74a2ef27 |
completed | March 12, 2026, 8:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b27911ff1481908a36f279a871c510 |
completed | March 12, 2026, 8:28 a.m. |
Created at: March 8, 2026, 3:07 p.m.