Triple
T7313400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mwotlap |
E168149
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object | Lemerig |
E155132
|
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: Lemerig | Statement: [Mwotlap, closelyRelatedTo, Lemerig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lemerig Context triple: [Mwotlap, closelyRelatedTo, Lemerig]
-
A.
Lemerig
chosen
Lemerig is an endangered Oceanic language spoken by a small community on the island of Vanua Lava in northern Vanuatu.
-
B.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
C.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
-
D.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
-
E.
Lebrun
Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
- 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_69c6888d8e3c81909db79714903baf31 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ec02319c819096d25e3683943886 |
completed | March 27, 2026, 8:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7e56f4aa0819096d955e2ce298299 |
completed | March 28, 2026, 2:27 p.m. |
Created at: March 27, 2026, 3:02 p.m.