Triple
T10950830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niendorf |
E258720
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Langenhorn |
E922574
|
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: Langenhorn | Statement: [Niendorf, borderedBy, Langenhorn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langenhorn Context triple: [Niendorf, borderedBy, Langenhorn]
-
A.
Langenhorn
chosen
Langenhorn is a residential quarter in the northern part of Hamburg, Germany, known for its green spaces and suburban character.
-
B.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
C.
Langenhain
Langenhain is a district of the town Hofheim am Taunus in the German state of Hesse, known for its residential character and proximity to the Taunus hills.
-
D.
Leuenberg
Leuenberg is a village in Switzerland known as the site where major European Protestant churches concluded the Leuenberg Agreement on church fellowship.
-
E.
Geiersthal
Geiersthal is a small municipality in the Bavarian Forest region of southeastern Germany.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770fc156c8190826e124c13ce7242 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e890bb9c81908c316a6423e650e6 |
completed | April 20, 2026, 8:49 a.m. |
Created at: April 8, 2026, 9:23 p.m.