Triple
T36371566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Men In Love |
E895768
|
entity |
| Predicate | containsFictionalEditor |
P85278
|
FINISHED |
| Object | Sydney Workman |
—
|
NE NERFINISHED |
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: Sydney Workman | Statement: [Old Men In Love, containsFictionalEditor, Sydney Workman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsFictionalEditor Context triple: [Old Men In Love, containsFictionalEditor, Sydney Workman]
-
A.
hasFictionalEditor
chosen
Indicates that an entity is associated with a fictional editor character responsible for editing or overseeing its content within a narrative or fictional context.
-
B.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
C.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
-
D.
hasFictionalIssue
Indicates that one entity possesses, is associated with, or is characterized by a particular fictional problem, flaw, or complication.
-
E.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
- 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_69f76e5115588190ad8738860b7bc68b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a01289f781481908f3788f8a719f2f4 |
completed | May 11, 2026, 12:53 a.m. |
| PD | Predicate disambiguation | batch_6a012823c7248190961e20be48dd6246 |
completed | May 11, 2026, 12:51 a.m. |
Created at: May 3, 2026, 4:10 p.m.