Triple
T5359290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gellert Grindelwald |
E102778
|
entity |
| Predicate | built |
P1028
|
FINISHED |
| Object | Nurmengard |
E514106
|
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: Nurmengard | Statement: [Gellert Grindelwald, built, Nurmengard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nurmengard Context triple: [Gellert Grindelwald, built, Nurmengard]
-
A.
Nurmengard
chosen
Nurmengard is a high-security wizarding prison built by the dark wizard Gellert Grindelwald in the Harry Potter universe, later used to confine Grindelwald himself.
-
B.
Mauregard
Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
-
C.
Vytegra
Vytegra is a small town in northwestern Russia known as a regional center near Lake Onega and the White Sea–Baltic Canal.
-
D.
Syrgenstein
Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
-
E.
Thalheim
Thalheim is a town in the German state of Saxony-Anhalt that was incorporated into the larger city of Bitterfeld-Wolfen.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86330e4c8190b5452226886287b3 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf291c89c8819084b9305c3bddc3c0 |
completed | March 21, 2026, 11:26 p.m. |
Created at: March 20, 2026, 2:02 p.m.