Triple
T10670916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eiserner Steg |
E251481
|
entity |
| Predicate | crosses |
P416
|
FINISHED |
| Object | River Main |
E113003
|
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: River Main | Statement: [Eiserner Steg, crosses, River Main]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: River Main Context triple: [Eiserner Steg, crosses, River Main]
-
A.
River Main
chosen
The River Main is a major waterway in central Germany that flows through cities such as Frankfurt before joining the Rhine.
-
B.
Spring River
Spring River is a tributary of the Arkansas River that flows through parts of Kansas, Missouri, and Oklahoma in the central United States.
-
C.
Kako River
The Kako River is a river in Japan whose name was used for the Imperial Japanese Navy cruiser Kako.
-
D.
Long River
Long River is the English translation of the Chinese name for the Yangtze, Asia’s longest river and a major waterway in China.
-
E.
Real River
Real River is a watercourse in the Brazilian state of Sergipe, known for flowing through the northeastern region of the country and contributing to local ecosystems and communities.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f86390648190851693aedce6b7ad |
completed | April 9, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998b77db08190bce5a7ce24dbc085 |
completed | April 11, 2026, 12:41 a.m. |
Created at: April 8, 2026, 9:09 p.m.