Triple
T22458626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gokarna beaches |
E555175
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Baada 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: Baada Beach | Statement: [Gokarna beaches, hasPart, Baada Beach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baada Beach Context triple: [Gokarna beaches, hasPart, Baada Beach]
-
A.
Baada Beach
chosen
Baada Beach is a scenic, relatively secluded coastal stretch near Kumta in Karnataka, India, known for its clean sands and tranquil Arabian Sea views.
-
B.
Laiya Beach
Laiya Beach is a popular white-sand beach destination in the Philippines known for its clear waters, resorts, and water activities.
-
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.
Mutun Beach
Mutun Beach is a popular coastal tourist destination in Lampung, Indonesia, known for its sandy shoreline, calm waters, and views of nearby small islands.
-
E.
Serasa Beach
Serasa Beach is a popular coastal recreation area in Brunei known for its sandy shoreline, water sports, and seaside leisure facilities.
- 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_69e11e51fdec8190adfdf9f8a6362221 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b7e01fc8190825c3dc024484440 |
completed | April 29, 2026, 1:14 a.m. |
Created at: April 16, 2026, 8:48 p.m.