Triple
T7197757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tontemboan language |
E168657
|
entity |
| Predicate | hasShiftTo |
P7790
|
FINISHED |
| Object | Indonesian language |
—
|
LITERAL 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: Indonesian language | Statement: [Tontemboan language, hasShiftTo, Indonesian language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShiftTo Context triple: [Tontemboan language, hasShiftTo, Indonesian language]
-
A.
hasShiftPressureFrom
Indicates that one entity experiences or is subjected to shift-related pressure originating from another entity.
-
B.
hasHistoricalShiftTo
chosen
Indicates a change over time in which one state, condition, or configuration is replaced or transformed into another in a historically traceable way.
-
C.
usesShiftCharacter
Indicates that an entity employs or requires the use of a shift (modifier) character, such as for capitalization or accessing alternate symbols.
-
D.
hasHistoricalShiftFrom
Indicates a relationship where one state, practice, or condition has been replaced or transformed over time from another earlier state, practice, or condition.
-
E.
hasDynamicShifts
Indicates that something exhibits changes or transitions in state, intensity, or behavior over time rather than remaining constant.
- F. None of above.
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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92a5d288190955f703470e75bf3 |
completed | March 27, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:52 p.m.