Triple
T9151163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Léonide Massine |
E219586
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Borken |
E604598
|
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: Borken | Statement: [Léonide Massine, placeOfDeath, Borken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borken Context triple: [Léonide Massine, placeOfDeath, Borken]
-
A.
Borken
chosen
Borken is a town in western Germany that serves as an administrative and commercial center in the state of North Rhine-Westphalia.
-
B.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
C.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
D.
Hagen
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
-
E.
Insterburg
Insterburg was a historically significant town in former East Prussia, now known as Chernyakhovsk in Russia’s Kaliningrad Oblast.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96b57888190a09a563f1fa522f6 |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0483cdd048190904260c44a5810cf |
completed | April 3, 2026, 11:07 p.m. |
Created at: March 30, 2026, 7:20 p.m.