Triple
T1125646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucson |
E24712
|
entity |
| Predicate | sisterCity |
P1072
|
FINISHED |
| Object | Pécs |
E38480
|
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: Pécs | Statement: [Tucson, sisterCity, Pécs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pécs Context triple: [Tucson, sisterCity, Pécs]
-
A.
Pécs
chosen
Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
-
B.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
C.
Siófok
Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
-
D.
Pozsony
Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
-
E.
Szeged
Szeged is a prominent city in southern Hungary known for its university, paprika production, and distinctive Art Nouveau architecture.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbdaf2d4819086f480f69da127f9 |
completed | March 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc97fe2808190b421329ed239af6f |
completed | March 8, 2026, 7:09 p.m. |
Created at: March 1, 2026, 7:44 p.m.