Triple
T30785164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanzanian Sign Language |
E783936
|
entity |
| Predicate | hasLexiconOrigin |
P1754
|
FINISHED |
| Object | local home signs in Tanzania |
—
|
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: local home signs in Tanzania | Statement: [Tanzanian Sign Language, hasLexiconOrigin, local home signs in Tanzania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLexiconOrigin Context triple: [Tanzanian Sign Language, hasLexiconOrigin, local home signs in Tanzania]
-
A.
hasLexiconSource
Indicates that a lexical item or entry is derived from, documented in, or otherwise sourced from a particular lexicon or lexical resource.
-
B.
hasLexicalCategoryOfOrigin
Indicates that one entity originates from, or is derived based on, the lexical category (such as noun, verb, adjective, etc.) of another entity.
-
C.
hasLexiconPreservedIn
Indicates that the lexicon of one entity is preserved, recorded, or stored within another entity.
-
D.
hasLanguageOfOrigin
chosen
Indicates that one entity has its origin or source in the language specified by another entity.
-
E.
hasSourceLanguageForLoanwords
Indicates that a language serves as the original source from which loanwords are borrowed into another language.
- 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_69f224b213c8819083886073f90b647e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fcef654d588190b29ecc76678d1aa0 |
completed | May 7, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69fcecdb97f48190b382b7d13be92dc0 |
completed | May 7, 2026, 7:49 p.m. |
Created at: April 29, 2026, 8:41 p.m.