Triple
T17652827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atherton |
E429536
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Malanda |
—
|
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: Malanda | Statement: [Atherton, near, Malanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malanda Context triple: [Atherton, near, Malanda]
-
A.
Malanda
chosen
Malanda is a small rural town in Queensland, Australia, known for its dairy industry and proximity to waterfalls and rainforest on the Atherton Tablelands.
-
B.
Negombo
Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
-
C.
Karonga
Karonga is a town in northern Malawi located on the shores of Lake Malawi, known as a regional transport hub and archaeological site.
-
D.
Malangwa
Malangwa is a town in southeastern Nepal that serves as a local commercial hub near the border with India.
-
E.
Matunga
Matunga is a central Mumbai neighborhood known for its strong South Indian cultural presence, educational institutions, and historic residential character.
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46e3e0ae481908382570f802d8144 |
completed | April 19, 2026, 5:55 a.m. |
Created at: April 10, 2026, 6:05 a.m.