Triple
T10450744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berliner Bezirk Spandau |
E246414
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Hakenfelde |
E388661
|
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: Hakenfelde | Statement: [Berliner Bezirk Spandau, hasPart, Hakenfelde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakenfelde Context triple: [Berliner Bezirk Spandau, hasPart, Hakenfelde]
-
A.
Hakenfelde
chosen
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
-
B.
Ruhmannsfelden
Ruhmannsfelden is a small market town in the Bavarian Forest region of southeastern Germany.
-
C.
Hellefeld
Hellefeld is a village and district within the town of Sundern in the Hochsauerland region of North Rhine-Westphalia, Germany.
-
D.
Hasselfelde
Hasselfelde is a small town in the Harz region of central Germany, now incorporated into the municipality of Oberharz am Brocken.
-
E.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
- 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_69d4fe0a6a548190a54212912f618e4e |
completed | April 7, 2026, 12:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f08ec96fe88190b791e6f50f39173f |
completed | April 28, 2026, 10:41 a.m. |
Created at: April 6, 2026, 12:17 p.m.