Triple
T13544100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stichtse Vecht |
E323466
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Woerden |
—
|
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: Woerden | Statement: [Stichtse Vecht, borderedBy, Woerden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Woerden Context triple: [Stichtse Vecht, borderedBy, Woerden]
-
A.
Woerden
chosen
Woerden is a historic Dutch city and municipality in the central Netherlands, known for its medieval fortifications and traditional cheese market.
-
B.
Roosendaal
Roosendaal is a city in the southern Netherlands known as a regional center for commerce and transport near the Belgian border.
-
C.
Schoonhoven
Schoonhoven is a historic Dutch town in South Holland, renowned for its silver craftsmanship and picturesque riverside setting.
-
D.
Gorinchem
Gorinchem is a historic fortified city in the Netherlands known for its well-preserved city walls and picturesque old town.
-
E.
Barendrecht
Barendrecht is a suburban town in the western Netherlands, located just south of Rotterdam and known for its residential character and logistics industry.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafda36248190acabde65a88c5471 |
completed | April 12, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:45 p.m.