Triple
T37773233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Glenarvon |
E941610
|
entity |
| Predicate | isFictionalizedPortraitOf |
P120606
|
FINISHED |
| Object | Lord Byron |
—
|
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: Lord Byron | Statement: [Lord Glenarvon, isFictionalizedPortraitOf, Lord Byron]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalizedPortraitOf Context triple: [Lord Glenarvon, isFictionalizedPortraitOf, Lord Byron]
-
A.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
B.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
C.
hasFictionalAutobiographer
Indicates that an entity is associated with a fictional character who serves as its autobiographical narrator or self-describing author within a narrative.
-
D.
portraysFictionalized
Indicates that one entity represents or depicts another entity in a fictionalized or altered manner, rather than as a strictly accurate portrayal.
-
E.
portrayedFictionalVersionOf
chosen
Indicates that one entity depicted or acted as a fictionalized or altered version 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_69f76ee4431881908f87e8892a9f39f3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbaf1f648c8190b625f91679b6f2ee |
completed | May 6, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:19 p.m.