Triple
T7078050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yser Front |
E164870
|
entity |
| Predicate | nearPlace |
P36605
|
FINISHED |
| Object | Nieuwpoort |
E130261
|
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: Nieuwpoort | Statement: [Yser Front, nearPlace, Nieuwpoort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nieuwpoort Context triple: [Yser Front, nearPlace, Nieuwpoort]
-
A.
Nieuwpoort
chosen
Nieuwpoort is a coastal town in West Flanders, Belgium, known for its historic harbor and its strategic role in World War I.
-
B.
Milport
Milport is a fictional British parliamentary constituency featured in Patrick O’Brian’s Aubrey–Maturin historical naval novels.
-
C.
Wilhelmshaven
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
-
D.
Terneuzen port
Terneuzen port is a major Dutch seaport and industrial hub on the Western Scheldt, known for its role in maritime trade and access to the Ghent–Terneuzen Canal.
-
E.
Holland-on-Sea
Holland-on-Sea is a coastal town in Essex, England, known for its quiet residential character and sandy beaches along the North Sea.
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ee29288190a9a3fa7a6c713d8f |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7a31a43b481908e535afc393b242a |
completed | March 28, 2026, 9:44 a.m. |
Created at: March 27, 2026, 2:40 p.m.