Triple
T9414021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castle Road |
E226768
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Bayreuth |
E112998
|
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: Bayreuth | Statement: [Castle Road, passesThrough, Bayreuth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayreuth Context triple: [Castle Road, passesThrough, Bayreuth]
-
A.
Bayreuth
chosen
Bayreuth is a city in northern Bavaria, Germany, best known for its association with composer Richard Wagner and its annual Bayreuth Festival of his operas.
-
B.
Bad Wurzach
Bad Wurzach is a spa town in the Allgäu region of southern Germany, known for its moorland landscapes and therapeutic mud baths.
-
C.
Donaueschingen
Donaueschingen is a town in southwestern Germany, in the Black Forest region of Baden-Württemberg, known as one of the sources of the Danube River.
-
D.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
E.
Schwetzingen
Schwetzingen is a town in southwestern Germany renowned for its Baroque palace and extensive formal gardens.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68c680e48190be82e3829e8711f0 |
completed | April 1, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107b63cf48190a072e3434a7b85a8 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:47 p.m.