Triple
T15215764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennersdorf |
E363632
|
entity |
| Predicate | locatedNearBorderWith |
P224
|
FINISHED |
| Object | Slovenia |
E18381
|
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: Slovenia | Statement: [Jennersdorf, locatedNearBorderWith, Slovenia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slovenia Context triple: [Jennersdorf, locatedNearBorderWith, Slovenia]
-
A.
Slovenia
chosen
Slovenia is a Central European country known for its mountains, lakes, and historic cities, and is a member of both the European Union and the Eurozone.
-
B.
Croatia
Croatia is a southeastern European country on the Adriatic Sea, known for its historic coastal cities, thousands of islands, and status as a member of both the European Union and NATO.
-
C.
Tursko
Tursko is a historical settlement known primarily as the site of the medieval Sack of Tursko.
-
D.
Slovakia
Slovakia is a landlocked Central European country known for its mountainous landscapes, medieval castles, and membership in major international organizations such as the European Union and NATO.
-
E.
Styria
Styria is a federal state in southeastern Austria known for its capital Graz, diverse landscapes, and strong industrial and educational sectors.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed3286a2c8190ac73b68cefd0191d |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 3:11 a.m.