Triple
T15142201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemensstadt |
E361710
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Haselhorst |
E444749
|
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: Haselhorst | Statement: [Siemensstadt, locatedNear, Haselhorst]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haselhorst Context triple: [Siemensstadt, locatedNear, Haselhorst]
-
A.
Haselhorst
chosen
Haselhorst is a residential and industrial locality in the Spandau borough of Berlin, Germany, known for its housing estates and proximity to the River Havel.
-
B.
Hornhuizen
Hornhuizen is a small village in the province of Groningen in the northern Netherlands, known for its rural landscape and historic church.
-
C.
Tienhoven
Tienhoven is a small village in the Dutch province of Utrecht, known for its rural landscape and location within the municipality of Stichtse Vecht.
-
D.
Enkhuizen
Enkhuizen is a historic port town in the Dutch province of North Holland, known for its maritime heritage and well-preserved old center on the IJsselmeer.
-
E.
Stadshagen
Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
- 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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005c5c4248190b57234e3ccf2831b |
completed | April 15, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0139e10e94819092b71606dbe4f5d5 |
completed | May 11, 2026, 2:07 a.m. |
Created at: April 10, 2026, 3:07 a.m.