Triple
T11831407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trnava Region |
E281400
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Senica
Senica is a town in western Slovakia known as an industrial and administrative center within the Trnava Region.
|
E950109
|
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: Senica | Statement: [Trnava Region, hasCity, Senica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senica Context triple: [Trnava Region, hasCity, Senica]
-
A.
Metauro
Metauro is a river in the Marche region of eastern Italy, historically known as the site of the Battle of the Metaurus during the Second Punic War.
-
B.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
-
C.
Bratenahl
Bratenahl is a small, affluent lakeside village located along the Lake Erie shoreline within the Cleveland metropolitan area in Cuyahoga County, Ohio.
-
D.
Kahuta
Kahuta is a town in Pakistan’s Punjab province known for hosting the country’s primary nuclear research and enrichment facilities.
-
E.
Orinda
Orinda is a suburban city in Contra Costa County, California, known for its affluent residential character, wooded hills, and role as a commuter community in the San Francisco Bay Area.
- 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: Senica Triple: [Trnava Region, hasCity, Senica]
Generated description
Senica is a town in western Slovakia known as an industrial and administrative center within the Trnava Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Senica Target entity description: Senica is a town in western Slovakia known as an industrial and administrative center within the Trnava Region.
-
A.
Metauro
Metauro is a river in the Marche region of eastern Italy, historically known as the site of the Battle of the Metaurus during the Second Punic War.
-
B.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
-
C.
Bratenahl
Bratenahl is a small, affluent lakeside village located along the Lake Erie shoreline within the Cleveland metropolitan area in Cuyahoga County, Ohio.
-
D.
Kahuta
Kahuta is a town in Pakistan’s Punjab province known for hosting the country’s primary nuclear research and enrichment facilities.
-
E.
Orinda
Orinda is a suburban city in Contra Costa County, California, known for its affluent residential character, wooded hills, and role as a commuter community in the San Francisco Bay Area.
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62c95988190a45dbaa7001c8846 |
completed | April 10, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f16741d9a08190b6d6d5e59dfa41b8 |
completed | April 29, 2026, 2:04 a.m. |
| NEDg | Description generation | batch_69f170034b488190a6976d3333823caa |
completed | April 29, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f17805325881908b98eb9c8fc59778 |
completed | April 29, 2026, 3:16 a.m. |
Created at: April 8, 2026, 9:43 p.m.