Triple
T2534248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olodumare |
E56230
|
entity |
| Predicate | notTypically |
P40931
|
FINISHED |
| Object | represented in images |
—
|
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: represented in images | Statement: [Olodumare, notTypically, represented in images]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notTypically Context triple: [Olodumare, notTypically, represented in images]
-
A.
notTypicallyUsedFor
Indicates that something is generally not used for a particular purpose, function, or activity under normal circumstances.
-
B.
typicallyLack
Indicates that one entity is characteristically or usually without, or does not possess, another entity or attribute.
-
C.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
D.
doesNot
Indicates that a specified entity lacks, refrains from, or fails to perform a particular action or exhibit a particular property in relation to another entity or context.
-
E.
nonExample
Indicates that something is explicitly identified as not being an example or instance of a given concept, category, or pattern.
- 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd648487881908ce8ca22def77294 |
completed | March 7, 2026, 7:39 a.m. |
Created at: March 6, 2026, 9:47 p.m.