Triple
T5108848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Narbonne |
E115163
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Eger |
E338315
|
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: Eger | Statement: [Narbonne, twinTown, Eger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eger Context triple: [Narbonne, twinTown, Eger]
-
A.
Eger
chosen
Eger is a historic city in northern Hungary known for its baroque architecture, castle, and wine culture.
-
B.
Sátoraljaújhely
Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
-
C.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
D.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
E.
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.
- 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_69bd4440b3348190be1251fd8b7951f1 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75aa6b088190b02cdb66ec4a11f0 |
completed | March 20, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfc94eb08190bd6ed138acca3171 |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:41 p.m.