Triple
T4229889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irchelpark |
E94553
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Unterstrass |
E400364
|
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: Unterstrass | Statement: [Irchelpark, locatedIn, Unterstrass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Unterstrass Context triple: [Irchelpark, locatedIn, Unterstrass]
-
A.
Unterstrass
chosen
Unterstrass is a district in the city of Zurich, Switzerland, known for its residential character and proximity to the city center.
-
B.
Oststadt
Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
-
C.
Dorotheenstadt
Dorotheenstadt is a historic district in central Berlin, Germany, known for its cultural significance and notable institutions.
-
D.
Magniviertel
Magniviertel is a historic quarter in Braunschweig, Germany, known for its medieval street layout, half-timbered houses, and lively cultural and nightlife scene.
-
E.
Adlershof
Adlershof is a district in Berlin, Germany, known as a major science, technology, and media hub featuring research institutes, universities, and high-tech companies.
- 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e61ccc081909b880baf1d6a0f24 |
completed | March 12, 2026, 11:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5964cd0088190a97ca5278046ba7b |
completed | March 14, 2026, 5:09 p.m. |
Created at: March 12, 2026, 11:05 p.m.