Triple
T38407342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claymoore Psychiatric Hospital |
E901368
|
entity |
| Predicate | treatsFictionalCharacter |
P191154
|
FINISHED |
| Object | Lisa Rowe |
—
|
NE NERFINISHED |
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: Lisa Rowe | Statement: [Claymoore Psychiatric Hospital, treatsFictionalCharacter, Lisa Rowe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsFictionalCharacter Context triple: [Claymoore Psychiatric Hospital, treatsFictionalCharacter, Lisa Rowe]
-
A.
usesFictionalCharacters
Indicates that one entity incorporates or employs fictional characters in relation to another entity (e.g., in its content, branding, or activities).
-
B.
attendedByFictionalCharacter
Indicates that a fictional character is present at, participates in, or is an attendee of a particular event or gathering.
-
C.
worksWithFictionalCharacter
Indicates that one entity collaborates or interacts in a work-related context with another entity that is a fictional character.
-
D.
fictionalCharacter
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
E.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
- 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_69f76e61e79c81908b787d83b46ab92b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcda3699948190adb57625bae08091 |
completed | May 7, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69fcd8fd16d08190b0aca6e19a632e99 |
completed | May 7, 2026, 6:25 p.m. |
| PDg | Predicate description generation | batch_69fcda35dc048190a3c90e15230900e0 |
completed | May 7, 2026, 6:30 p.m. |
Created at: May 3, 2026, 4:31 p.m.