Triple
T14059739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heves County |
E338311
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Tiszanána
Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
|
E1078686
|
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: Tiszanána | Statement: [Heves County, contains, Tiszanána]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiszanána Context triple: [Heves County, contains, Tiszanána]
-
A.
Ciskei
Ciskei was a nominally independent Bantustan in southeastern South Africa established under apartheid as a homeland for Xhosa-speaking people.
-
B.
Kwaluseni
Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
-
C.
Thembuland
Thembuland is a historical region in South Africa that served as the traditional homeland of the Thembu people, an Nguni-speaking group to which figures like Nelson Mandela belong.
-
D.
Transkei, South Africa
Transkei, South Africa was a former bantustan in the southeastern part of the country, historically designated for Xhosa-speaking people during the apartheid era.
-
E.
Sekhukhuneland
Sekhukhuneland is a historical region in northeastern South Africa that served as the heartland of the Pedi kingdom and culture.
- 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: Tiszanána Triple: [Heves County, contains, Tiszanána]
Generated description
Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tiszanána Target entity description: Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
-
A.
Ciskei
Ciskei was a nominally independent Bantustan in southeastern South Africa established under apartheid as a homeland for Xhosa-speaking people.
-
B.
Kwaluseni
Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
-
C.
Thembuland
Thembuland is a historical region in South Africa that served as the traditional homeland of the Thembu people, an Nguni-speaking group to which figures like Nelson Mandela belong.
-
D.
Transkei, South Africa
Transkei, South Africa was a former bantustan in the southeastern part of the country, historically designated for Xhosa-speaking people during the apartheid era.
-
E.
Sekhukhuneland
Sekhukhuneland is a historical region in northeastern South Africa that served as the heartland of the Pedi kingdom and culture.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5686f51c81908c33143ecbaae83d |
completed | April 14, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb662c37c8190a629278a97060080 |
completed | May 7, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_69fcc99fca8c8190bbcafba5bacfdfda |
completed | May 7, 2026, 5:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcca3a375c819092b3f67612d2ec0c |
completed | May 7, 2026, 5:22 p.m. |
Created at: April 9, 2026, 10:21 p.m.