Triple
T9703614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pest County |
E234839
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Érd
Érd is a rapidly growing city in central Hungary that functions as a suburban residential and commercial hub southwest of Budapest.
|
E815464
|
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: Érd | Statement: [Pest County, hasSettlement, Érd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Érd Context triple: [Pest County, hasSettlement, Érd]
-
A.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
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.
Mezőkeresztes
Mezőkeresztes is a town in northeastern Hungary historically notable as the site of a major 1596 battle between Ottoman and Habsburg forces.
-
D.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
E.
Gyöngyös
Gyöngyös is a historic town in northern Hungary known as a gateway to the Mátra mountain range and its surrounding wine-producing region.
- 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: Érd Triple: [Pest County, hasSettlement, Érd]
Generated description
Érd is a rapidly growing city in central Hungary that functions as a suburban residential and commercial hub southwest of Budapest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Érd Target entity description: Érd is a rapidly growing city in central Hungary that functions as a suburban residential and commercial hub southwest of Budapest.
-
A.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
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.
Mezőkeresztes
Mezőkeresztes is a town in northeastern Hungary historically notable as the site of a major 1596 battle between Ottoman and Habsburg forces.
-
D.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
E.
Gyöngyös
Gyöngyös is a historic town in northern Hungary known as a gateway to the Mátra mountain range and its surrounding wine-producing region.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d73a0148190ad4178fd462cdd9c |
completed | April 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19132687c8190baf3a60af1b789a8 |
completed | April 4, 2026, 10:31 p.m. |
| NEDg | Description generation | batch_69d193150c00819080ed0fbb050b60bf |
completed | April 4, 2026, 10:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d19416efd48190865d0178e5e893fa |
completed | April 4, 2026, 10:43 p.m. |
Created at: March 30, 2026, 8:18 p.m.