Triple
T7500379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hann. Münden |
E177241
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Kassel |
E210960
|
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: Kassel | Statement: [Hann. Münden, locatedNear, Kassel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kassel Context triple: [Hann. Münden, locatedNear, Kassel]
-
A.
Kassel
chosen
Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
-
B.
Gießen
Gießen is a mid-sized university city in central Germany known for its academic institutions and role as a regional administrative and cultural center.
-
C.
Erfurt
Erfurt is a historic German city in the state of Thuringia, known for its well-preserved medieval old town and as an important cultural and educational center.
-
D.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
E.
Karlsruhe
Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f598dfac8190a123daaac0784aee |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc60b0ae48190bb6e6f38bcbb05dd |
completed | April 2, 2026, 1:27 a.m. |
Created at: March 27, 2026, 3:44 p.m.