Triple
T19028303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Halki |
E465667
|
entity |
| Predicate | hasBeach |
P1922
|
FINISHED |
| Object | Kania Beach |
—
|
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: Kania Beach | Statement: [Halki, hasBeach, Kania Beach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kania Beach Context triple: [Halki, hasBeach, Kania Beach]
-
A.
Kania Beach
chosen
Kania Beach is a small, tranquil seaside spot on the Greek island of Chalki, known for its clear waters and relaxed atmosphere.
-
B.
Patenga Beach
Patenga Beach is a popular seaside tourist spot near the port city of Chittagong in southeastern Bangladesh, known for its coastal views and proximity to the Bay of Bengal.
-
C.
Baina Beach
Baina Beach is a popular coastal stretch in Goa, India, known for its scenic shoreline, water sports, and proximity to the port town of Vasco da Gama.
-
D.
Koki Beach
Koki Beach is a scenic red-sand beach on Maui’s Hāna coast, known for its rugged shoreline, strong surf, and views of the nearby ʻAlau Island.
-
E.
Juhu Beach
Juhu Beach is a popular and expansive seaside destination in Mumbai, India, known for its lively atmosphere, street food stalls, and views of the Arabian Sea.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d73ec9088190a98e214bd56e8622 |
completed | April 20, 2026, 7:35 a.m. |
Created at: April 10, 2026, 12:02 p.m.