Triple
T10244270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miniatur Wunderland |
E232791
|
entity |
| Predicate | hasSection |
P35
|
FINISHED |
| Object | USA |
E391575
|
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: USA | Statement: [Miniatur Wunderland, hasSection, USA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: USA Context triple: [Miniatur Wunderland, hasSection, USA]
-
A.
USA
USA is a public research university located in Mobile, Alabama, known for its diverse academic programs and regional impact in the Gulf Coast area.
-
B.
USA
USA is the three-letter International Olympic Committee country code representing the United States of America in international sporting events.
-
C.
US
US is the commonly used abbreviation for the University of Szczecin, a public higher education institution in Szczecin, Poland.
-
D.
US
US is the commonly used abbreviation for the University of Seville, a major public research university located in Seville, Spain.
-
E.
US
chosen
The US, or United States, is a large federal republic in North America composed of 50 states and known as one of the world's most influential economic and political powers.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22a76188190a73df23bfb08eb3d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f6e73a2881908563e9e6a02df944 |
completed | April 9, 2026, 12:46 a.m. |
Created at: April 6, 2026, 11:26 a.m.