Triple
T7239199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiritimati |
E155310
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Banana |
E606581
|
NE FINISHED |
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: Banana | Statement: [Kiritimati, hasSettlement, Banana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Banana Context triple: [Kiritimati, hasSettlement, Banana]
-
A.
Banana
Banana is a British television drama series created by Russell T Davies that explores the lives and relationships of LGBTQ+ characters in contemporary Manchester.
-
B.
Banana
"Banana" is a comedic animated short film set in the Despicable Me universe, featuring the Minions in a slapstick adventure centered around their obsession with bananas.
-
C.
Banana
chosen
Banana is a port town in the Democratic Republic of the Congo situated on the Atlantic coast at the mouth of the Congo River.
-
D.
Bananas
Bananas is a 1971 satirical comedy film directed by and starring Woody Allen, known for its absurd political humor and farcical take on revolution in a fictional Latin American country.
-
E.
Cavendish bananas
Cavendish bananas are the globally dominant commercial banana variety, known for their sweet flavor, seedless flesh, and suitability for large-scale export.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c688143bfc81908d4176617735e601 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea37fa9081908e9c3abe49d151e5 |
completed | March 27, 2026, 8:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cc3d75b48190916bf327396f2666 |
completed | March 28, 2026, 12:40 p.m. |
Created at: March 27, 2026, 2:55 p.m.