Triple
T2316998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walter Schellenberg |
E51087
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Saarbrücken |
E269297
|
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: Saarbrücken | Statement: [Walter Schellenberg, placeOfBirth, Saarbrücken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saarbrücken Context triple: [Walter Schellenberg, placeOfBirth, Saarbrücken]
-
A.
Saarbrücken
chosen
Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
-
B.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
C.
Koblenz
Koblenz is a historic German city in Rhineland-Palatinate, known for its strategic location at the confluence of the Rhine and Moselle rivers and its well-preserved fortresses and old town.
-
D.
Homburg
Homburg is a town in southwestern Germany known as an administrative and economic center within the state of Saarland.
-
E.
Diekirch
Diekirch is a town in northern Luxembourg known for its role in World War II, particularly during the country's liberation, and for its national military museum.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc62df2048190ac7a5ebc0a4139b2 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce777e5081909ed7e3e60bf33503 |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 4, 2026, 7:49 p.m.