Triple
T9113891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lupita Nyong'o as Patsey |
E218671
|
entity |
| Predicate | portrayalCharacteristics |
P49090
|
FINISHED |
| Object | physically demanding |
—
|
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: physically demanding | Statement: [Lupita Nyong'o as Patsey, portrayalCharacteristics, physically demanding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalCharacteristics Context triple: [Lupita Nyong'o as Patsey, portrayalCharacteristics, physically demanding]
-
A.
portrayalFeature
chosen
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
C.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
D.
portraysCharacterInGenre
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
E.
portrayalLanguage
Indicates the language in which something is depicted, represented, or expressed.
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca84c209c8190b082a9b8499bedf7 |
completed | April 1, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69cc65fe5be081909d4470d6317b14a6 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:16 p.m.