Triple
T17203576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norddeutscher Lloyd |
E417540
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bremen |
—
|
NE NERFINISHED |
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: Bremen | Statement: [Norddeutscher Lloyd, locatedIn, Bremen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bremen Context triple: [Norddeutscher Lloyd, locatedIn, Bremen]
-
A.
Bremen
chosen
Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
-
B.
Bremen
Bremen is a small village in Fairfield County, Ohio, known for its historic charm and tight-knit rural community.
-
C.
Bremen
Bremen is a small city in western Georgia, United States, known as a regional hub along major transportation routes and as part of the Atlanta metropolitan area’s outer region.
-
D.
Braunschweig
Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
-
E.
Hannover
Hannover is a major city in northern Germany known as a regional economic and cultural center, hosting significant research institutions and trade fairs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42db11fc881908291bf29cc740e09 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 5:38 a.m.