Triple
T8751590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massa |
E207971
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Veszprém |
E168418
|
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: Veszprém | Statement: [Massa, twinnedWith, Veszprém]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Veszprém Context triple: [Massa, twinnedWith, Veszprém]
-
A.
Veszprém
chosen
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
B.
Szekszárd
Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
-
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.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
E.
Győr
Győr is a historic city in northwestern Hungary, known as an important regional cultural and economic center at the confluence of the Danube, Rába, and Rábca rivers.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da774f4819099e5bfd12973d946 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c6b99a3c8190b873b1cc94609ed6 |
completed | April 4, 2026, 8:07 a.m. |
Created at: March 30, 2026, 6:39 p.m.