Triple
T882656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karl Dönitz |
E19060
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Grünau |
E107699
|
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: Grünau | Statement: [Karl Dönitz, placeOfBirth, Grünau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grünau Context triple: [Karl Dönitz, placeOfBirth, Grünau]
-
A.
Gunzenhausen
Gunzenhausen is a historic town in Bavaria, Germany, known for its location on the Altmühl River and as a gateway to the Franconian Lake District.
-
B.
Feuchtwangen
Feuchtwangen is a historic town in Bavaria, Germany, known for its medieval architecture and location along the Romantic Road.
-
C.
Friedenau
chosen
Friedenau is a residential district in southwestern Berlin known for its historic architecture, leafy streets, and literary heritage.
-
D.
Heidenheim an der Brenz
Heidenheim an der Brenz is a town in the German state of Baden-Württemberg known for its industrial heritage, historic castle Hellenstein, and location on the Brenz River near the Swabian Jura.
-
E.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
- 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4accda4148190aa628dab14d7f5de |
completed | March 1, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acaca531348190b47f98bc825b1307 |
completed | March 7, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:39 p.m.