Triple
T3259378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyhavn |
E68372
|
entity |
| Predicate | hasNotableBuilding |
P1544
|
FINISHED |
| Object | Nyhavn 9 |
E68372
|
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: Nyhavn 9 | Statement: [Nyhavn, hasNotableBuilding, Nyhavn 9]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyhavn 9 Context triple: [Nyhavn, hasNotableBuilding, Nyhavn 9]
-
A.
Nyhavn
chosen
Nyhavn is a historic waterfront district in central Copenhagen known for its colorful 17th-century townhouses, canalside restaurants, and vibrant harbor atmosphere.
-
B.
New Havener
A New Havener is a resident or native of New Haven, Connecticut, a historic coastal city known for being home to Yale University.
-
C.
The Wharf
The Wharf is a major mixed-use waterfront development in Washington, D.C., featuring residences, restaurants, entertainment venues, and public spaces along the Potomac River.
-
D.
The Boathouse
The Boathouse is a popular waterfront dining restaurant at Disney Springs known for its nautical theme, fresh seafood, and vintage amphicar rides.
-
E.
The Boathouse
The Boathouse is a popular lakeside restaurant and event venue in St. Louis’s Forest Park, known for its scenic views and boat rentals.
- 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_69ad858f74408190bcbd07f967cd7bd0 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adafa4f40c81909adfd0f7f568e3ce |
completed | March 8, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28ed941cc81909c35853e793d6ce5 |
completed | March 12, 2026, 10 a.m. |
Created at: March 8, 2026, 3:09 p.m.