Triple
T17143116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louny |
E416018
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Most |
E397576
|
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: Most | Statement: [Louny, hasNearbyCity, Most]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Most Context triple: [Louny, hasNearbyCity, Most]
-
A.
Most
chosen
Most is an industrial city in the Ústí nad Labem Region of the Czech Republic, historically known for coal mining and extensive postwar urban redevelopment.
-
B.
MOST
MOST is a science and technology museum in Syracuse, New York, featuring interactive exhibits and educational programs focused on STEM learning.
-
C.
MOST
MOST is the commonly used acronym for the Chinese Ministry of Science and Technology, the central government body responsible for national science and technology policy and innovation strategy in China.
-
D.
Meiste
Meiste is a village-level subdivision of the town of Rüthen in the district of Soest, North Rhine-Westphalia, Germany.
-
E.
Much
Much is a municipality in the Rhein-Sieg district of North Rhine-Westphalia, Germany, known for its rural character and scenic landscapes in the Bergisches Land region.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f2d73c3c81908b875023bb925edb |
completed | April 18, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01415718c88190834fedae7b01ac69 |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:36 a.m.