Triple
T32527096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angelina Jolie as Lisa Rowe |
E831344
|
entity |
| Predicate | characterDiagnosis |
P198525
|
FINISHED |
| Object | sociopathy |
—
|
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: sociopathy | Statement: [Angelina Jolie as Lisa Rowe, characterDiagnosis, sociopathy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterDiagnosis Context triple: [Angelina Jolie as Lisa Rowe, characterDiagnosis, sociopathy]
-
A.
characterReveals
Indicates that one character discloses or makes known information, feelings, or intentions to another character.
-
B.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
C.
characterDescription
Indicates that one entity provides a textual description or portrayal of the characteristics, traits, or attributes of another entity.
-
D.
characterResolution
Indicates the process or outcome by which a character’s conflicts, arcs, or internal struggles are brought to a conclusion or clarified within a narrative.
-
E.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
- 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_69f34923e1548190be0524205d8cdf8f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fef112398081909237c3872345968b |
completed | May 9, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69feefb14ec08190ab401987d8c84a23 |
completed | May 9, 2026, 8:26 a.m. |
| PDg | Predicate description generation | batch_69fef1112b0c8190a8eb027edb71e4e0 |
completed | May 9, 2026, 8:32 a.m. |
Created at: May 1, 2026, 1:01 a.m.