Triple
T15309013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Six Degrees of Kevin Bacon |
E365977
|
entity |
| Predicate | hasAssociatedField |
P106841
|
FINISHED |
| Object | cinema |
—
|
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: cinema | Statement: [Six Degrees of Kevin Bacon, hasAssociatedField, cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedField Context triple: [Six Degrees of Kevin Bacon, hasAssociatedField, cinema]
-
A.
hasRelatedField
chosen
Indicates that one field is associated with or connected to another field in a relevant or contextually meaningful way.
-
B.
hasFieldContribution
Indicates that an entity has made a contribution or provided input within a particular field, domain, or area of activity.
-
C.
hasBaseField
Indicates that one entity serves as the foundational or underlying field structure upon which another entity is defined or constructed.
-
D.
hasFieldName
Indicates that one entity is associated with, or identified by, a specific field name in a data structure or schema.
-
E.
hasAssociatedPosition
Indicates that one entity is linked to a specific role, job, or spatial/organizational position associated with it.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03cd176708190b0f6ba17aed92f8e |
completed | April 16, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.