Triple
T5035844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaggenau |
E113418
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Rastatt |
E108471
|
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: Rastatt | Statement: [Gaggenau, locatedNear, Rastatt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rastatt Context triple: [Gaggenau, locatedNear, Rastatt]
-
A.
Rastatt
chosen
Rastatt is a historic town in southwestern Germany, known for its Baroque architecture and its role as the site of significant early 18th-century peace negotiations.
-
B.
Rottweil
Rottweil is a historic town in southwestern Germany known for its medieval architecture and as the namesake of the Rottweiler dog breed.
-
C.
Pforzheim
Pforzheim is a city in southwestern Germany, historically known for its jewelry and watchmaking industry and its heavy destruction during World War II.
-
D.
Albstadt
Albstadt is a town in the Swabian Jura region of Baden-Württemberg, Germany, known for its textile industry, scenic hiking and cycling routes, and role as a regional economic center.
-
E.
Freudenstadt
Freudenstadt is a spa and holiday town in southwestern Germany known for its large market square and location in the northern Black Forest.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b9ad488190a2a8c4da8858eb91 |
completed | March 20, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20ce295f081909046cbc278ee6183 |
completed | March 24, 2026, 4:02 a.m. |
Created at: March 20, 2026, 1:36 p.m.