Triple
T18310809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Terevaka |
E438618
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Rano Kau |
—
|
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: Rano Kau | Statement: [Mount Terevaka, near, Rano Kau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rano Kau Context triple: [Mount Terevaka, near, Rano Kau]
-
A.
Rano Kau
chosen
Rano Kau is a large volcanic crater on the southwestern tip of Easter Island, notable for its steep cliffs, crater lake, and archaeological remains of the Rapa Nui culture.
-
B.
Rano
Rano is a historic town and traditional emirate in northern Nigeria, located within Kano State.
-
C.
Rano
Rano is a dialect of the Uripiv-Wala-Rano-Atchin language cluster spoken in Vanuatu.
-
D.
ʻĀina Haina
ʻĀina Haina is a residential neighborhood on the southeastern coast of Oʻahu, Hawaiʻi, known for its suburban character and proximity to Honolulu.
-
E.
Taneti Maamau
Taneti Maamau is a Kiribati politician who has served as the country's president, known for his pro-China foreign policy stance and focus on economic development and climate resilience.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e502180d208190ae7c4f3d0ef3dc55 |
completed | April 19, 2026, 4:26 p.m. |
Created at: April 10, 2026, 10:36 a.m.