Triple
T30536638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Three Faces of Eve |
E777165
|
entity |
| Predicate | disorderDepicted |
P92869
|
FINISHED |
| Object | multiple personality disorder |
—
|
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: multiple personality disorder | Statement: [The Three Faces of Eve, disorderDepicted, multiple personality disorder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disorderDepicted Context triple: [The Three Faces of Eve, disorderDepicted, multiple personality disorder]
-
A.
exampleOfDisorder
chosen
Indicates that one entity is an instance or specific case of a particular disorder represented by another entity.
-
B.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
D.
hasPsychologicalCondition
Indicates that an entity experiences or is diagnosed with a particular psychological or mental health condition.
-
E.
interpretsDiseaseAs
Indicates that one entity understands, explains, or conceptualizes a disease in terms of another entity (such as a model, category, cause, or interpretation).
- 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_69f2249d183c8190b79937c1768d2163 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68850f3088190b84f1b63101d47e9 |
completed | May 2, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69f67e42d6688190b60e91d2c388c555 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 8:18 p.m.