Triple
T17820506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fichtelgebirge |
E444968
|
entity |
| Predicate | riverSourceOf |
P4102
|
FINISHED |
| Object | Red Main |
—
|
NE NERFINISHED |
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: Red Main | Statement: [Fichtelgebirge, riverSourceOf, Red Main]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Main Context triple: [Fichtelgebirge, riverSourceOf, Red Main]
-
A.
Red Main
chosen
The Red Main is a river in northern Bavaria, Germany, that flows through the city of Bayreuth and forms one of the two headstreams of the Main River.
-
B.
Red Town
Red Town is a historical region associated with the settlement of Krasnaya Sloboda, known for its cultural and regional significance.
-
C.
Shout
Shout is a household cleaning brand best known for its stain-removal products for laundry and fabrics.
-
D.
Shout
"Shout" is a classic 1959 rhythm and blues song by The Isley Brothers that became an enduring party anthem and pop culture staple.
-
E.
Shout
Shout is a film written by Joe Gayton, best known as a rock-and-roll–infused drama set in the 1950s about a rebellious music teacher who transforms a small-town Texas boys’ school.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48910eb8881908db8ec08e2752d7d |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 10, 2026, 10:15 a.m.