Triple
T11829796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warao language |
E281355
|
entity |
| Predicate | hasVoicelessStops |
P101713
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Warao language, hasVoicelessStops, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVoicelessStops Context triple: [Warao language, hasVoicelessStops, true]
-
A.
hasNasalConsonants
Indicates that the subject language or word includes one or more nasal consonant sounds in its phonological inventory or pronunciation.
-
B.
hasConsonantPhonemes
Indicates that an entity possesses or includes one or more consonant phonemes in its phonological system.
-
C.
distinguishesVoicelessUnaspiratedConsonants
Indicates the ability to perceive or mark a difference between consonant sounds that are voiceless and unaspirated and other types of consonants.
-
D.
hasEjectiveConsonants
Indicates that a language’s consonant inventory includes ejective consonants, produced with a glottalic egressive airstream mechanism.
-
E.
hasConsonantManner
Indicates that one sound is related to another by sharing a specific manner of consonant articulation (e.g., stop, fricative, nasal).
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62b75dc8190b27d24e46a262a11 |
completed | April 10, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:43 p.m.