Triple
T35439055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enga language |
E1024290
|
entity |
| Predicate | hasPrenasalizedStops |
P206986
|
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: [Enga language, hasPrenasalizedStops, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrenasalizedStops Context triple: [Enga language, hasPrenasalizedStops, true]
-
A.
hasNasalConsonants
Indicates that the subject language or word includes one or more nasal consonant sounds in its phonological inventory or pronunciation.
-
B.
hasVoicelessStops
Indicates that the subject language or sound system includes voiceless stop consonants (such as [p], [t], [k]) in its phonemic inventory.
-
C.
hasVoicelessNasals
Indicates that the subject possesses or exhibits voiceless nasal sounds (nasal consonants produced without vocal fold vibration).
-
D.
hasNasalVowels
Indicates that the subject language or phonological system includes vowels that are produced with nasal airflow (nasalized vowels).
-
E.
hasPalatalizationContrast
Indicates that a language distinguishes meaning between sounds based on whether or not they are palatalized, treating palatalization as a contrastive phonological feature.
- 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_69f76df743c48190aecb6dd79efb0d95 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:04 p.m.