Triple
T25538205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arty Froushan |
E640100
|
entity |
| Predicate | portraysFictionalCharacterIn |
P33556
|
FINISHED |
| Object | Carnival Row |
—
|
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: Carnival Row | Statement: [Arty Froushan, portraysFictionalCharacterIn, Carnival Row]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysFictionalCharacterIn Context triple: [Arty Froushan, portraysFictionalCharacterIn, Carnival Row]
-
A.
portraysFictionalEntity
chosen
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
B.
portraysCharacterInGenre
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
C.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
D.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
E.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
- 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_69e75dbfff7081909b0aa779d48321d2 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 21, 2026, 3:24 p.m.