Triple
T31554324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Candy |
E805086
|
entity |
| Predicate | portraysRealPeople |
P107820
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Candy, portraysRealPeople, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysRealPeople Context triple: [Candy, portraysRealPeople, true]
-
A.
portraysFictionalized
Indicates that one entity represents or depicts another entity in a fictionalized or altered manner, rather than as a strictly accurate portrayal.
-
B.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
C.
basedOnRealLife
Indicates that something is derived from, inspired by, or directly adapted from actual real-world events, people, or situations.
-
D.
featuresRealPersonAsHimself
chosen
Indicates that a real person appears in the work portraying themself rather than a fictional character.
-
E.
portrayalKnownFor
Indicates that a particular portrayal or role is the one for which an entity is especially recognized or famous.
- 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_69f348d22e088190ad555d5bd42f9da0 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: April 30, 2026, 10:12 p.m.