Triple
T14703638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Tui |
E345368
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Motunui |
E1115780
|
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: Motunui | Statement: [Chief Tui, residence, Motunui]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Motunui Context triple: [Chief Tui, residence, Motunui]
-
A.
Motunui
chosen
Motunui is the fictional Polynesian island homeland of Moana in Disney's animated film "Moana."
-
B.
Mijas
Mijas is a picturesque municipality in the province of Málaga in southern Spain, known for its whitewashed village, coastal resorts, and location along the Costa del Sol.
-
C.
Monjo
Monjo is a small Sherpa village in Nepal’s Khumbu region that serves as a common stop for trekkers on the route to Everest Base Camp.
-
D.
Mottama
Mottama is a historic port town in southeastern Myanmar, long known as Martaban, which was once an important trading center in the region.
-
E.
Tamasopo
Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb6071e5c8190bb5509c859135c2d |
completed | April 14, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24a996708190834733bfc669c3d3 |
completed | May 8, 2026, 6 p.m. |
Created at: April 10, 2026, 1:28 a.m.