Triple
T4432886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaas Dijkhoff |
E95374
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Breda |
E151867
|
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: Breda | Statement: [Klaas Dijkhoff, residence, Breda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Breda Context triple: [Klaas Dijkhoff, residence, Breda]
-
A.
Breda
chosen
Breda is a historic city in the southern Netherlands known for its medieval architecture, former status as a military and political center, and vibrant cultural life.
-
B.
Breda
Breda is an Italian industrial company best known for manufacturing railway rolling stock, including trains and trams used in transit systems worldwide.
-
C.
Arvato
Arvato is a global business process outsourcing and services provider specializing in customer relationship management, supply chain management, and digital solutions.
-
D.
Barth
Barth is a surname most notably associated with American actress Jessica Barth, known for her role in the "Ted" film series.
-
E.
Brugg
Brugg is a historic Swiss town in the canton of Aargau, known for its medieval heritage and strategic location near the confluence of the Aare, Reuss, and Limmat rivers.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3556cd83881908547aa311c4f17fa |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6137171148190b77a6f783d5cf315 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:31 p.m.