Triple
T17388201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heinersdorf |
E422742
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Blankenburg |
—
|
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: Blankenburg | Statement: [Heinersdorf, adjacentTo, Blankenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blankenburg Context triple: [Heinersdorf, adjacentTo, Blankenburg]
-
A.
Blankenburg
chosen
Blankenburg is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and village-like atmosphere.
-
B.
Blankenburg
Blankenburg is a town in central Germany, located in the Harz region of the state of Saxony-Anhalt.
-
C.
Kronenburg
Kronenburg is a tram and metro stop in Amstelveen, Netherlands, serving the Amsterdam metro/Tram 25 line and the surrounding residential and commercial area.
-
D.
Nunspeet
Nunspeet is a Dutch town and municipality on the Veluwe known for its forests, heathlands, and role as a popular nature and holiday destination.
-
E.
Molenschot
Molenschot is a small village in the Dutch province of North Brabant, known for its rural character and traditional local community.
- 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a8b66288190b29bb82eff761902 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.