Triple
T12134460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanau |
E289017
|
entity |
| Predicate | locatedOn |
P40
|
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: [Hanau, locatedOn, River Main]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: River Main Context triple: [Hanau, locatedOn, 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.
Train River
The Train River is a smaller watercourse in Belgium that serves as a tributary within the Dyle River basin.
-
D.
Town River
Town River is a small river in southeastern Massachusetts that flows through communities such as West Bridgewater before joining the Taunton River system.
-
E.
Kako River
The Kako River is a river in Japan whose name was used for the Imperial Japanese Navy cruiser Kako.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9158c59e0819094d4522a107482b2 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f68c8ee081908a0331805f62cb0d |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:49 p.m.