Triple
T21938483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damanese Portuguese |
E541754
|
entity |
| Predicate | morphosyntaxInfluenceFrom |
P95957
|
FINISHED |
| Object | South Asian languages |
—
|
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: South Asian languages | Statement: [Damanese Portuguese, morphosyntaxInfluenceFrom, South Asian languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: morphosyntaxInfluenceFrom Context triple: [Damanese Portuguese, morphosyntaxInfluenceFrom, South Asian languages]
-
A.
hasLexicalInfluenceOn
Indicates that one linguistic element (such as a word, phrase, or lexicon) has affected or shaped the form, usage, or meaning of another linguistic element.
-
B.
linguisticInfluence
Indicates that one entity has affected, shaped, or contributed to the language, style, or linguistic features of another entity.
-
C.
hasSyntaxInfluenceFrom
chosen
Indicates that the syntax of one entity is influenced, shaped, or derived from the syntax of another entity.
-
D.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
E.
influencesLanguageOf
Indicates that one entity affects, shapes, or alters the language used by another entity.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1241e35bc81909eb3225d5cd97b92 |
completed | April 28, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e6f5efc208819091ed2cf6841fa600 |
completed | April 21, 2026, 3:58 a.m. |
Created at: April 16, 2026, 7:55 p.m.