Triple
T20745322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M43 motorway (Hungary) |
E510564
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Makó |
—
|
NE NERFINISHED |
How this triple was built (2 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: Makó | Statement: [M43 motorway (Hungary), passesNear, Makó]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makó Context triple: [M43 motorway (Hungary), passesNear, Makó]
-
A.
Makó
chosen
Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
-
B.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
C.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
D.
Karcag
Karcag is a town in eastern Hungary known for its Great Hungarian Plain agricultural traditions and historic Calvinist heritage.
-
E.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c212b744819089bc1d47a020bd61 |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:33 p.m.