Triple
T14819825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert D. San Souci |
E348416
|
entity |
| Predicate | hasWrittenForAgeGroup |
P90488
|
FINISHED |
| Object | children |
—
|
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: children | Statement: [Robert D. San Souci, hasWrittenForAgeGroup, children]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWrittenForAgeGroup Context triple: [Robert D. San Souci, hasWrittenForAgeGroup, children]
-
A.
hasWrittenForChildren
chosen
Indicates that an entity has authored or created written works specifically intended for children as the target audience.
-
B.
hasWrittenFor
Indicates that one entity has created written content (such as articles, stories, or texts) for or on behalf of another entity, typically a publication, organization, or platform.
-
C.
performedForAgeGroup
Indicates that an action or performance is specifically intended for, targeted at, or carried out on behalf of a particular age group.
-
D.
intendedForAgeGroup
Indicates that something is designed, suitable, or targeted for use by a specific age group.
-
E.
canAgeFor
Indicates that one entity is capable of undergoing an aging or maturation process for the benefit, use, or context of another entity.
- 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decfe4cf38819090f25ef045351d5d |
completed | April 14, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:50 a.m.