Triple
T10441676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruhr reservoir system |
E246184
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Harkortsee |
E317106
|
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: Harkortsee | Statement: [Ruhr reservoir system, hasPart, Harkortsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harkortsee Context triple: [Ruhr reservoir system, hasPart, Harkortsee]
-
A.
Harkortsee
chosen
Harkortsee is an artificial lake and recreational reservoir on the Ruhr River in North Rhine-Westphalia, Germany, popular for water sports and leisure activities.
-
B.
Langer See
Langer See is a long, narrow lake in southeastern Berlin that forms part of the city’s interconnected Spree–Dahme waterway and is popular for boating and watersports.
-
C.
Plau am See
Plau am See is a small town and lakeside resort in the Mecklenburg Lake District of northern Germany, known for its natural scenery and water-based recreation.
-
D.
Weißer See
Weißer See is a small urban lake and popular recreational spot located in Berlin's Weißensee district.
-
E.
Tyresö-Flaten Lake
Tyresö-Flaten Lake is a natural lake in Tyresö Municipality, Sweden, known for its scenic surroundings and recreational opportunities such as swimming and outdoor activities.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fb9ebf488190ae776bd65e94cb00 |
completed | April 7, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbd966f0f08190a60ca3bcf0e08e98 |
completed | April 12, 2026, 5:41 p.m. |
Created at: April 6, 2026, 12:15 p.m.