Triple
T9363307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenderes |
E225331
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Karcag |
E687173
|
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: Karcag | Statement: [Kenderes, locatedNear, Karcag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karcag Context triple: [Kenderes, locatedNear, Karcag]
-
A.
Karcag
chosen
Karcag is a town in eastern Hungary known for its Great Hungarian Plain agricultural traditions and historic Calvinist heritage.
-
B.
Kőszeg
Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
-
C.
Makó
Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
-
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.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
- 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_69ca842bdd648190904131d58620d448 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd503ddf8c81908b090afa54ec5e6d |
completed | April 1, 2026, 5:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100d3444c81908b14f165ec128b76 |
completed | April 4, 2026, 12:15 p.m. |
Created at: March 30, 2026, 7:42 p.m.