Triple
T16226313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kabinawa |
E393856
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Abemama |
E93413
|
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: Abemama | Statement: [Kabinawa, locatedIn, Abemama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abemama Context triple: [Kabinawa, locatedIn, Abemama]
-
A.
Abemama
chosen
Abemama is a central Pacific atoll in the island nation of Kiribati, known for its lagoon, traditional villages, and role in the country’s colonial and wartime history.
-
B.
Bibemi
Bibemi is a town located in the North Region of Cameroon, known as a local administrative and trading center in the area.
-
C.
Amuesha
Amuesha is another name for the Yaneshaʼ language, an Arawakan language spoken by the Yaneshaʼ (Amuesha) people of central Peru.
-
D.
Mumei
Mumei is a central character known for her mysterious, emissary-like role within the narrative of *The Emissary*.
-
E.
Abena
"Abena" is a popular Afrobeats song by Ghanaian singer King Promise, known for its smooth melodies and romantic lyrics.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d26b02c819080b70ab7cc3bcc24 |
completed | April 17, 2026, 2:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00354a20d081908288fb8c0e8b83b6 |
completed | May 10, 2026, 7:35 a.m. |
Created at: April 10, 2026, 5:03 a.m.