Triple
T7059737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Köpenick |
E164184
|
entity |
| Predicate | hasBodyOfWater |
P1778
|
FINISHED |
| Object | Spree |
E34242
|
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: Spree | Statement: [Köpenick, hasBodyOfWater, Spree]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spree Context triple: [Köpenick, hasBodyOfWater, Spree]
-
A.
Spree
chosen
The Spree is a major river in eastern Germany that flows through the heart of Berlin and is central to the city's landscape and history.
-
B.
Spree
Spree is a dark satirical horror-thriller film about a rideshare driver obsessed with social media fame, starring Joe Keery.
-
C.
Shopify
Shopify is a leading global e-commerce platform that enables businesses to create and manage online stores and sell products across multiple channels.
-
D.
WooCommerce
WooCommerce is a widely used open-source eCommerce plugin for WordPress that enables users to create and manage online stores.
-
E.
Magento
Magento is an open-source e-commerce platform widely used by businesses to build and manage online stores with extensive customization and scalability.
- 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_69c688796c148190adb2f1596f595f22 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e458ad9c81908c3f492b317ce291 |
completed | March 27, 2026, 8:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c788a8c4b481908193ffc795b75796 |
completed | March 28, 2026, 7:52 a.m. |
Created at: March 27, 2026, 2:38 p.m.