Triple
T20128135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 826 Valencia |
E490810
|
entity |
| Predicate | hasNotableFounderOccupation |
P13522
|
FINISHED |
| Object | author |
—
|
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: author | Statement: [826 Valencia, hasNotableFounderOccupation, author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFounderOccupation Context triple: [826 Valencia, hasNotableFounderOccupation, author]
-
A.
hasNotableFounder
Indicates that an entity was founded or established by a person or organization considered especially significant or noteworthy.
-
B.
notableCoFounderOccupation
Indicates that the co-founder is particularly notable or recognized for having a specific occupation.
-
C.
coFounderNotableFor
Indicates that a person is a co-founder of something for which they are notably recognized.
-
D.
hasNotableProfessionDistributionIn
Indicates that the distribution or prevalence of notable professions associated with an entity is observed or characterized within a specified context, such as a location or group.
-
E.
hasNotableBearerOccupation
chosen
Indicates that an entity is associated with a notable person who holds a specific occupation.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6675fe3d48190b0c20b483a951e68 |
completed | April 20, 2026, 5:50 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:31 p.m.