Triple
T3262376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elector of Hesse |
E68440
|
entity |
| Predicate | hasTerritory |
P285
|
FINISHED |
| Object | Hanau |
E289017
|
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: Hanau | Statement: [Elector of Hesse, hasTerritory, Hanau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanau Context triple: [Elector of Hesse, hasTerritory, Hanau]
-
A.
Hanau
chosen
Hanau is a town in the German state of Hesse, known as an important regional center and the birthplace of the Brothers Grimm.
-
B.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
C.
Herrlingen
Herrlingen is a small village in the German state of Baden-Württemberg, historically noted as the place where Field Marshal Erwin Rommel spent his final days during World War II.
-
D.
Schaafheim
Schaafheim is a municipality in the state of Hesse in central Germany.
-
E.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
- 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_69ad8590444081909e8107a8aeef3a23 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafa908e881908cbb2ad137819ffb |
completed | March 8, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e83a7a508190afd5342c79f3da9d |
completed | March 12, 2026, 4:22 p.m. |
Created at: March 8, 2026, 3:09 p.m.