Triple
T20686662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quiripi language |
E508436
|
entity |
| Predicate | hasReconstructionFocus |
P141049
|
FINISHED |
| Object | phonology |
—
|
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: phonology | Statement: [Quiripi language, hasReconstructionFocus, phonology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReconstructionFocus Context triple: [Quiripi language, hasReconstructionFocus, phonology]
-
A.
hasRestorationFocus
Indicates that something is primarily concerned with or directed toward restoration or repair.
-
B.
hasReconstructionPurpose
Indicates that something exists or is performed with the specific aim or function of reconstruction (e.g., rebuilding, restoring, or re-creating something).
-
C.
hasReconstructionLevel
Indicates the degree or stage to which something has been rebuilt, restored, or reconstructed.
-
D.
hasReconstructionType
Indicates the specific method or category of reconstruction applied to an object, structure, or dataset.
-
E.
hasReconstructedFeatures
Indicates that certain features of an entity have been rebuilt, restored, or inferred from incomplete or damaged original data.
- F. None of above. chosen
Provenance (4 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6beacc30081908e57cd7f2c3ef047 |
completed | April 21, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69e5c03caee881908be4dd25796a03d5 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 11:45 a.m.