Triple
T11651302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRVG |
E276909
|
entity |
| Predicate | underlyingCompanyHeadquartersLocation |
P22347
|
FINISHED |
| Object | Düsseldorf, Germany |
E36993
|
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: Düsseldorf, Germany | Statement: [TRVG, underlyingCompanyHeadquartersLocation, Düsseldorf, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Düsseldorf, Germany Context triple: [TRVG, underlyingCompanyHeadquartersLocation, Düsseldorf, Germany]
-
A.
Düsseldorf
chosen
Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
-
B.
Krefeld, Germany
Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
-
C.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
-
D.
Frankfort, Germany
Frankfort, Germany is a German city whose name has been used for places abroad, including the village of Frankfort in Illinois, USA.
-
E.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a2cea9308190a13f7dd995ea07a4 |
completed | April 10, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee8805a91081909ff29b3357e351ee |
completed | April 26, 2026, 9:47 p.m. |
Created at: April 8, 2026, 9:39 p.m.