Triple
T9990263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rummelsburger See |
E196867
|
entity |
| Predicate | isConnectedTo |
P845
|
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: [Rummelsburger See, isConnectedTo, Spree]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spree Context triple: [Rummelsburger See, isConnectedTo, 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.
Zoho Commerce
Zoho Commerce is an e-commerce platform that enables businesses to create, manage, and grow online stores with integrated tools for website building, payments, inventory, and order management.
- 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_69ca82f1678c819093d06320a05f16a4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdc7a0cb6481908d7bd1b43f93bd18 |
completed | April 2, 2026, 1:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26a21b2388190b16f0aa142846599 |
completed | April 5, 2026, 1:56 p.m. |
Created at: March 30, 2026, 8:50 p.m.