Triple
T6647007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tonga people |
E150726
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Batonga |
E570267
|
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: Batonga | Statement: [Tonga people, alsoKnownAs, Batonga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Batonga Context triple: [Tonga people, alsoKnownAs, Batonga]
-
A.
Tanambogo
Tanambogo is a small island in the Central Province of the Solomon Islands, notable for its role as a Japanese seaplane base and site of intense fighting during World War II.
-
B.
Tolitoli
Tolitoli is a coastal town and regency capital in Central Sulawesi, Indonesia, known as a regional hub for trade and agriculture.
-
C.
Bitonga
chosen
Bitonga is a Bantu language spoken primarily by the Bitonga people in Mozambique’s Inhambane Province.
-
D.
Unawatuna
Unawatuna is a popular coastal town in southern Sri Lanka known for its palm-fringed beach, coral-rich bay, and laid-back tourist atmosphere.
-
E.
Karanga
Karanga is a major dialect of the Shona language spoken primarily in southern Zimbabwe, known for its distinct phonological and lexical features.
- 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_69c687f1a3048190828b7342f7125d5c |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b01cecc48190a6d2c26d8d5ab80c |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eef6def8819084bccdf6f11e63da |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2 p.m.