Triple
T15551490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Széll Kálmán tér |
E370753
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Margit körút |
E1123467
|
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: Margit körút | Statement: [Széll Kálmán tér, near, Margit körút]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margit körút Context triple: [Széll Kálmán tér, near, Margit körút]
-
A.
Margit körút
chosen
Margit körút is a major boulevard in Budapest, Hungary, known for connecting the Buda side’s central districts and serving as an important traffic and public transport artery near the Danube.
-
B.
Margit híd stop
Margit híd stop is a tram station in Budapest located near the Margaret Bridge, serving as a key interchange point on the city's tram network.
-
C.
Margareta
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
-
D.
Margarida
Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
-
E.
Majgull
Majgull is a Swedish given name, notably borne by the acclaimed author Majgull Axelsson.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a9551288190a583e8291c35f521 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c3e67c881909a9fa1e483a364be |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:08 a.m.