Triple
T18879795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robeson Channel |
E461794
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Greenland |
—
|
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: Greenland | Statement: [Robeson Channel, borderedBy, Greenland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greenland Context triple: [Robeson Channel, borderedBy, Greenland]
-
A.
Greenland
chosen
Greenland is the world’s largest island, an autonomous territory within the Kingdom of Denmark, known for its vast Arctic landscapes and extensive ice sheet.
-
B.
Greenland
Greenland is a 2020 American disaster thriller film starring Gerard Butler, centered on a family's struggle to survive a catastrophic comet event.
-
C.
Groenlandia
Groenlandia is a film and television production company known for working on major international projects such as the series "Game of Thrones."
-
D.
Grenland
Grenland is a culturally and historically significant region in southeastern Norway, centered around the industrial towns near the coast and traditionally associated with the county of Telemark.
-
E.
Grønland
Grønland is a central Oslo neighborhood known for its multicultural character, vibrant street life, and diverse shops and eateries.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d06ef481908bba297d7a1fd011 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.