Triple
T30787853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | influenced by Malay |
E784002
|
entity |
| Predicate | typicalBorrowedDomains |
P207308
|
FINISHED |
| Object | trade vocabulary |
—
|
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: trade vocabulary | Statement: [influenced by Malay, typicalBorrowedDomains, trade vocabulary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBorrowedDomains Context triple: [influenced by Malay, typicalBorrowedDomains, trade vocabulary]
-
A.
typicalDomain
Indicates that one entity is the characteristic or most common domain, context, or area of application in which another entity typically occurs or is used.
-
B.
laterDomain
Indicates that one domain or time interval occurs strictly after another in a temporal ordering.
-
C.
relatedTLD
Indicates that one top-level domain (TLD) has an association or connection with another TLD, such as similarity, shared purpose, or contextual relevance.
-
D.
previousSharedTLD
Indicates that two entities have previously shared the same top-level domain (TLD) in their web addresses.
-
E.
usedAsDomainHack
Indicates that a domain name is intentionally chosen or formatted so that its full text (including the top-level domain) forms a meaningful word or phrase, effectively "hacking" the domain structure for semantic or branding purposes.
- 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_69f224b213c8819083886073f90b647e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:41 p.m.