Triple
T18192472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hagestein weir |
E435572
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hagestein |
—
|
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: Hagestein | Statement: [Hagestein weir, locatedNear, Hagestein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagestein Context triple: [Hagestein weir, locatedNear, Hagestein]
-
A.
Hagestein
chosen
Hagestein is a small village in the Dutch province of Utrecht, known for its historic church and rural character along the Lek River.
-
B.
Bohlweg
Bohlweg is a major central street in Braunschweig, Germany, known as an important urban thoroughfare and commercial area.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
-
E.
Elsterberg
Elsterberg is a small town in the Vogtland region of Saxony, Germany, known for its historic castle ruins and scenic location along the White Elster River.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e0d05974819094b4a50d081be881 |
completed | April 19, 2026, 2:04 p.m. |
Created at: April 10, 2026, 10:31 a.m.