Triple
T11156871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erin Hannon |
E263931
|
entity |
| Predicate | replacesCharacterInRole |
P98140
|
FINISHED |
| Object | Pam Beesly as receptionist |
—
|
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: Pam Beesly as receptionist | Statement: [Erin Hannon, replacesCharacterInRole, Pam Beesly as receptionist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: replacesCharacterInRole Context triple: [Erin Hannon, replacesCharacterInRole, Pam Beesly as receptionist]
-
A.
characterRoleSwap
Indicates a relationship where two characters exchange or assume each other’s narrative roles or functions within a story or scenario.
-
B.
removedCharacter
Indicates that a character was taken out or deleted from a text, sequence, or collection.
-
C.
covertRole
Indicates that an entity holds or performs a hidden, secret, or undercover role in relation to another entity or context.
-
D.
playRoleIn
Indicates that an entity participates in or performs a specific function, character, or part within an event, context, or system.
-
E.
treatsCharacter
Indicates how one character behaves toward or interacts with another character, especially in terms of care, respect, or mistreatment.
- 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_69d6aa9ccddc8190868998c8b7beb060 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8741cd48190b7cc29c6b6bc54ff |
completed | April 9, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69d75cec26fc8190a5497d186306f935 |
completed | April 9, 2026, 8:01 a.m. |
| PDg | Predicate description generation | batch_69d7706116248190a87440bec3960884 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:28 p.m.