Triple
T2055793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Austria |
E45670
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Vienna Woods
The Vienna Woods is a forested highland region in eastern Austria known for its natural beauty, hiking trails, and role as a green belt near Vienna.
|
E228792
|
NE FINISHED |
How this triple was built (4 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: Vienna Woods | Statement: [Lower Austria, contains, Vienna Woods]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienna Woods Context triple: [Lower Austria, contains, Vienna Woods]
-
A.
Wiesen
Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
-
B.
Lustgarten
Lustgarten is a historic public park and square on Berlin’s Museum Island, long used as a parade ground and gathering place.
-
C.
Belvedere
Belvedere is an affluent, scenic waterfront city in Marin County, California, known for its views of San Francisco Bay and upscale residential character.
-
D.
Plauen
Plauen is a historic town in eastern Germany known for its textile industry and intricate lace production.
-
E.
Islington Woods
Islington Woods is a residential neighbourhood in the city of Vaughan, Ontario, known for its green spaces and suburban character.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Vienna Woods Triple: [Lower Austria, contains, Vienna Woods]
Generated description
The Vienna Woods is a forested highland region in eastern Austria known for its natural beauty, hiking trails, and role as a green belt near Vienna.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vienna Woods Target entity description: The Vienna Woods is a forested highland region in eastern Austria known for its natural beauty, hiking trails, and role as a green belt near Vienna.
-
A.
Wiesen
Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
-
B.
Lustgarten
Lustgarten is a historic public park and square on Berlin’s Museum Island, long used as a parade ground and gathering place.
-
C.
Belvedere
Belvedere is an affluent, scenic waterfront city in Marin County, California, known for its views of San Francisco Bay and upscale residential character.
-
D.
Plauen
Plauen is a historic town in eastern Germany known for its textile industry and intricate lace production.
-
E.
Islington Woods
Islington Woods is a residential neighbourhood in the city of Vaughan, Ontario, known for its green spaces and suburban character.
- F. None of above. chosen
Provenance (5 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_69a8891a19508190a12ef1e192308dcb |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9a9ce548190a5a3488fafb2e79e |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae200eb09881908bbfe47ebb62f55e |
completed | March 9, 2026, 1:19 a.m. |
| NEDg | Description generation | batch_69ae20cb479c8190853d0d954af16887 |
completed | March 9, 2026, 1:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae21614e74819093617a355f0857c8 |
completed | March 9, 2026, 1:24 a.m. |
Created at: March 4, 2026, 7:40 p.m.