Triple
T20005917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Child of Deaf Adults |
E494460
|
entity |
| Predicate | typicalBiculturalStatus |
P138330
|
FINISHED |
| Object | bicultural in Deaf and hearing cultures |
—
|
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: bicultural in Deaf and hearing cultures | Statement: [Child of Deaf Adults, typicalBiculturalStatus, bicultural in Deaf and hearing cultures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBiculturalStatus Context triple: [Child of Deaf Adults, typicalBiculturalStatus, bicultural in Deaf and hearing cultures]
-
A.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
B.
isBinational
Indicates that an entity is associated with or recognized by two distinct nations, such as holding dual nationality or operating under the authority of two countries.
-
C.
majorityBilingualWith
Indicates that the majority of individuals in a given group or population are bilingual in the two specified languages or language groups.
-
D.
bilingualName
Indicates that an entity has a name expressed in two different languages, linking the entity to its bilingual designation.
-
E.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
- 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a57ef881909115c8aa232b1012 |
completed | April 20, 2026, 5:25 p.m. |
| PD | Predicate disambiguation | batch_69e54cdddbd48190becc8b2aa5ab4ef9 |
completed | April 19, 2026, 9:45 p.m. |
| PDg | Predicate description generation | batch_69e54fc20888819083c9118a09d0d2dc |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 11, 2026, 3:33 p.m.