Triple
T1915571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iris Steensma |
E40008
|
entity |
| Predicate | portrayalLedTo |
P33121
|
FINISHED |
| Object | Academy Award nomination for Jodie Foster |
—
|
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: Academy Award nomination for Jodie Foster | Statement: [Iris Steensma, portrayalLedTo, Academy Award nomination for Jodie Foster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalLedTo Context triple: [Iris Steensma, portrayalLedTo, Academy Award nomination for Jodie Foster]
-
A.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
B.
portrayedByWork
Indicates that a work (such as a film, book, or artwork) depicts, represents, or portrays a particular entity.
-
C.
challengesPortrayalOf
Indicates that one entity questions, disputes, or undermines the way another entity is represented or depicted.
-
D.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
E.
portrayedInWork
Indicates that an entity is depicted or represented as a character, figure, or subject within a specific creative work.
- F. None of above. chosen
Provenance (4 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1e517e8819086e4bf5a305aeb25 |
completed | March 7, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69abafed2ab481908920334e77b1021b |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb1b180e481908bbe893d6ba6208b |
completed | March 7, 2026, 5:03 a.m. |
Created at: March 4, 2026, 7:35 p.m.