Triple
T20672813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M4 motorway (Hungary) |
E508072
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Püspökladány |
—
|
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: Püspökladány | Statement: [M4 motorway (Hungary), passesNear, Püspökladány]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Püspökladány Context triple: [M4 motorway (Hungary), passesNear, Püspökladány]
-
A.
Püspökladány
chosen
Püspökladány is a town in eastern Hungary known for its location on the Great Hungarian Plain and its role as a local agricultural and transport hub.
-
B.
Pusztaszabolcs
Pusztaszabolcs is a small town in central Hungary known for its role as a local railway junction and residential community within Fejér County.
-
C.
Nagykálló
Nagykálló is a town in northeastern Hungary known for its historical architecture and traditional cultural heritage.
-
D.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
E.
Bonyhád
Bonyhád is a town in southern Hungary known as an important local center within Tolna County.
- 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_69e0b4c1164881909a3bf1e3ddb2bc32 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5cb1fc88190805f623e93a70368 |
completed | April 20, 2026, 11:24 p.m. |
Created at: April 16, 2026, 11:44 a.m.