Triple
T12643323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nigerian English |
E301953
|
entity |
| Predicate | typicalWord |
P56045
|
FINISHED |
| Object | chop (meaning eat or spend freely) |
—
|
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: chop (meaning eat or spend freely) | Statement: [Nigerian English, typicalWord, chop (meaning eat or spend freely)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWord Context triple: [Nigerian English, typicalWord, chop (meaning eat or spend freely)]
-
A.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
B.
typicalTerm
chosen
Indicates that something is a standard, representative, or characteristic term typically associated with a given concept or context.
-
C.
typicalWordFormation
Indicates the usual or most common way in which a word is formed from other linguistic elements (such as roots, affixes, or compounds).
-
D.
typicalSymbol
Indicates that something serves as a characteristic or commonly recognized symbol representing something else.
-
E.
word1
Indicates that there is a first word in a sequence or pair that participates in the specified relationship.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960b47130819097e1162ed4fc993a |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:17 p.m.