Triple
T7500497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verden (Aller) |
E177243
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Verden |
E177243
|
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: Verden | Statement: [Verden (Aller), shortName, Verden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verden Context triple: [Verden (Aller), shortName, Verden]
-
A.
Verden
chosen
Verden is a historic town in Lower Saxony, Germany, known for its medieval cathedral and location along the Weser River.
-
B.
Świecie
Świecie is a historic town in northern Poland, located in the Kuyavian-Pomeranian Voivodeship and known for its medieval castle and position near the confluence of the Vistula and Wda rivers.
-
C.
Erdek
Erdek is a coastal town and popular seaside resort in Turkey’s Balıkesir Province, located on the Kapıdağ Peninsula along the Sea of Marmara.
-
D.
Terra
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
E.
Maa
Maa is a Nilotic language spoken primarily by the Maasai people of Kenya and Tanzania.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f598dfac8190a123daaac0784aee |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c9490fc81908d35c0537b45aa13 |
completed | March 28, 2026, 8:39 p.m. |
Created at: March 27, 2026, 3:44 p.m.