Triple
T15355392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Comnidyne |
E367159
|
entity |
| Predicate | hasFictionalPolicy |
P58963
|
FINISHED |
| Object | strict workplace rules |
—
|
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: strict workplace rules | Statement: [Comnidyne, hasFictionalPolicy, strict workplace rules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalPolicy Context triple: [Comnidyne, hasFictionalPolicy, strict workplace rules]
-
A.
hasNotablePolicy
Indicates that an entity possesses a policy that is distinguished, significant, or otherwise noteworthy in its context.
-
B.
hasFictionalPractice
Indicates that an entity engages in, is associated with, or features a practice, activity, or procedure that exists only within a fictional or imagined context.
-
C.
hasFictionalProperty
chosen
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
-
D.
hasFictionalFunction
Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
-
E.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:18 a.m.