Triple
T15215812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Burgenland |
E363633
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Frauenkirchen
Frauenkirchen is a small town in eastern Austria known for its baroque basilica and location in the Seewinkel region near Lake Neusiedl.
|
E1143491
|
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: Frauenkirchen | Statement: [Northern Burgenland, contains, Frauenkirchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frauenkirchen Context triple: [Northern Burgenland, contains, Frauenkirchen]
-
A.
Schweitenkirchen
Schweitenkirchen is a municipality in Bavaria, Germany, situated in the district of Pfaffenhofen an der Ilm.
-
B.
Lorenzkirch
Lorenzkirch is a small village in Saxony, Germany, known historically as the birthplace of Nobel Prize–winning physicist Wolfgang Paul.
-
C.
Oberkirch
Oberkirch is a town in the Ortenau district of Baden-Württemberg in southwestern Germany, known for its wine production and picturesque location at the edge of the Black Forest.
-
D.
Nunkirchen
Nunkirchen is a village and district of the town of Wadern in the Saarland region of western Germany.
-
E.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
- 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: Frauenkirchen Triple: [Northern Burgenland, contains, Frauenkirchen]
Generated description
Frauenkirchen is a small town in eastern Austria known for its baroque basilica and location in the Seewinkel region near Lake Neusiedl.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frauenkirchen Target entity description: Frauenkirchen is a small town in eastern Austria known for its baroque basilica and location in the Seewinkel region near Lake Neusiedl.
-
A.
Schweitenkirchen
Schweitenkirchen is a municipality in Bavaria, Germany, situated in the district of Pfaffenhofen an der Ilm.
-
B.
Lorenzkirch
Lorenzkirch is a small village in Saxony, Germany, known historically as the birthplace of Nobel Prize–winning physicist Wolfgang Paul.
-
C.
Oberkirch
Oberkirch is a town in the Ortenau district of Baden-Württemberg in southwestern Germany, known for its wine production and picturesque location at the edge of the Black Forest.
-
D.
Nunkirchen
Nunkirchen is a village and district of the town of Wadern in the Saarland region of western Germany.
-
E.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
- 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_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_69fed343f51481908f04c35d37b39ad2 |
completed | May 9, 2026, 6:25 a.m. |
| NEDg | Description generation | batch_69fed44b2e3c8190aad111e2bc2b56a2 |
completed | May 9, 2026, 6:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed547192c8190b89755fff48ca620 |
completed | May 9, 2026, 6:33 a.m. |
Created at: April 10, 2026, 3:11 a.m.