Triple
T4045421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lent |
E84053
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Nijmegen |
E13123
|
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: Nijmegen | Statement: [Lent, partOf, Nijmegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nijmegen Context triple: [Lent, partOf, Nijmegen]
-
A.
Nijmegen
chosen
Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
-
B.
Utrecht
Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
-
C.
Tilburg
Tilburg is a city in the southern Netherlands known historically as an industrial and textile center and now as a regional cultural and educational hub.
-
D.
Eindhoven
Eindhoven is a major city in the southern Netherlands known for its industrial and technological significance, particularly as a hub for electronics and design.
-
E.
Apeldoorn
Apeldoorn is a city in the province of Gelderland in the Netherlands, known for the royal palace Het Loo and its historical ties to the Dutch monarchy.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb5f85d48190ba80a0a24fbe438a |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d181f4123c8190a15ee03b3156c6fd |
completed | April 4, 2026, 9:26 p.m. |
Created at: March 9, 2026, 3:37 p.m.