Triple
T11319455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meg Tilly as Sister Agnes |
E268052
|
entity |
| Predicate | traumaTheme |
P98487
|
FINISHED |
| Object | repressed memories |
—
|
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: repressed memories | Statement: [Meg Tilly as Sister Agnes, traumaTheme, repressed memories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traumaTheme Context triple: [Meg Tilly as Sister Agnes, traumaTheme, repressed memories]
-
A.
trauma
Indicates that an entity has experienced a deeply distressing or harmful event or series of events that cause lasting psychological or emotional impact.
-
B.
traumaLevel
Indicates the degree or severity of trauma experienced or present in relation to an entity or event.
-
C.
otherMajorTragedy
Indicates that the subject experienced or was involved in a significant tragic event other than the primary or most notable tragedy under consideration.
-
D.
livedAfterAssault
Indicates that the subject continued to live for some period of time following the occurrence of an assault.
-
E.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9de875481908acfa56015d4b46f |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787ad575081908274280bf75d95fd |
completed | April 9, 2026, 11:04 a.m. |
| PDg | Predicate description generation | batch_69d796d049e88190a9fd7508f477f541 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:32 p.m.