Triple
T6564998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakao |
E153880
|
entity |
| Predicate | isNeighboringLanguageOf |
P16383
|
FINISHED |
| Object | Tamambo |
E157536
|
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: Tamambo | Statement: [Sakao, isNeighboringLanguageOf, Tamambo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamambo Context triple: [Sakao, isNeighboringLanguageOf, Tamambo]
-
A.
Tamambo
chosen
Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
-
B.
Yambu
Yambu is a coastal city in western Saudi Arabia on the Red Sea, known as an important port and industrial center.
-
C.
Tallimba
Tallimba is a small rural locality in the Riverina region of New South Wales, Australia, situated within an agricultural area west of the town of West Wyalong.
-
D.
Tamalu
Tamalu is a village located on Car Nicobar Island in the Nicobar district of India’s Andaman and Nicobar Islands.
-
E.
Cembo
Cembo is a residential and commercial barangay in Makati City, Philippines, known for its dense urban community and proximity to major business districts.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d0a262808190a33ac94374affde4 |
completed | March 27, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d5622e0481909b0ac0f4e06d19bc |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:52 p.m.