Triple
T16027091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amstelveen |
E388742
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Óbuda-Békásmegyer |
E900042
|
NE FINISHED |
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: Óbuda-Békásmegyer | Statement: [Amstelveen, hasTwinTown, Óbuda-Békásmegyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Óbuda-Békásmegyer Context triple: [Amstelveen, hasTwinTown, Óbuda-Békásmegyer]
-
A.
Óbuda-Békásmegyer
chosen
Óbuda-Békásmegyer is a district in the northern part of Budapest, Hungary, known for its historic Óbuda area and residential neighborhoods along the Danube.
-
B.
Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
-
C.
Bóly
Bóly is a small town in southern Hungary known for its agricultural surroundings and location within Baranya County.
-
D.
Bonyhád
Bonyhád is a town in southern Hungary known as an important local center within Tolna County.
-
E.
Bükkszék
Bükkszék is a small village in northern Hungary known for its medicinal thermal waters and spa tourism.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18328707c8190b9a444c78faaaa04 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf33c6a881909284933ea3b7dd6e |
completed | May 10, 2026, 12:20 a.m. |
Created at: April 10, 2026, 4:56 a.m.