Triple
T13092505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antiguoko |
E310495
|
entity |
| Predicate | hasAgeGroups |
P100475
|
FINISHED |
| Object | under-teen and junior categories |
—
|
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: under-teen and junior categories | Statement: [Antiguoko, hasAgeGroups, under-teen and junior categories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgeGroups Context triple: [Antiguoko, hasAgeGroups, under-teen and junior categories]
-
A.
ageGroup
Indicates the categorical age range or bracket to which an entity belongs.
-
B.
containsAge
Indicates that one entity includes or specifies the age value or age-related information of another entity.
-
C.
numberOfAges
Indicates the count of distinct ages associated with an entity or within a specified group or context.
-
D.
ageGroupStructure
Indicates how a population or set of entities is distributed across different age groups or age categories.
-
E.
ageGroupSystem
chosen
Indicates a classification relationship where entities are grouped according to a defined system of age-based categories.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813acbac8190b2fe5e07287457cf |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:03 p.m.