Triple
T9364758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avellino |
E225371
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Schwechat |
E279069
|
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: Schwechat | Statement: [Avellino, twinnedWith, Schwechat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwechat Context triple: [Avellino, twinnedWith, Schwechat]
-
A.
Schwechat
chosen
Schwechat is an Austrian town just southeast of Vienna, best known as the site of Vienna International Airport and a major hub for industry and transport.
-
B.
Seibersdorf
Seibersdorf is an Austrian town known for hosting major research and testing laboratories of the International Atomic Energy Agency.
-
C.
Donaustadt
Donaustadt is the 22nd district of Vienna, Austria, known for its extensive residential areas, modern developments, and the location of the Vienna International Centre.
-
D.
Mödling
Mödling is a historic town in Lower Austria, near Vienna, known for its picturesque old town, wine culture, and proximity to the Vienna Woods.
-
E.
Pfersdorf
Pfersdorf is a village-level subdivision of the municipality of Roth in Bavaria, Germany.
- 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_69cd503fd7f081909655e2a880c84834 |
completed | April 1, 2026, 5:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12ccc3584819090a66cd50375c5af |
completed | April 4, 2026, 3:22 p.m. |
Created at: March 30, 2026, 7:42 p.m.