Triple
T10150666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maasdijk |
E232627
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Maassluis |
E119115
|
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: Maassluis | Statement: [Maasdijk, locatedNear, Maassluis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maassluis Context triple: [Maasdijk, locatedNear, Maassluis]
-
A.
Maassluis
chosen
Maassluis is a historic port town in the province of South Holland in the Netherlands, situated along the Nieuwe Waterweg west of Rotterdam.
-
B.
Hellevoetsluis
Hellevoetsluis is a historic Dutch port town known for its maritime heritage and coastal location in the western Netherlands.
-
C.
Hulst
Hulst is a historic fortified town and municipality in the Dutch province of Zeeland, near the border with Belgium.
-
D.
Onderdendam
Onderdendam is a small historic village in the Dutch province of Groningen, known for its canals, bridges, and traditional architecture.
-
E.
Bleiswijk
Bleiswijk is a village in the Dutch province of South Holland, known for its greenhouse horticulture and location within the municipality of Lansingerland.
- 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_69ca84885e48819088a31b127cf44904 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec0584a48190b65daa8370555c27 |
completed | April 2, 2026, 4:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76b9112948190a0dd747a67f8206a |
completed | May 3, 2026, 3:36 p.m. |
Created at: March 30, 2026, 9:08 p.m.