Triple
T24046605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Felicity Porter |
E595537
|
entity |
| Predicate | followsCharacterToCity |
P49434
|
FINISHED |
| Object | Ben Covington |
—
|
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: Ben Covington | Statement: [Felicity Porter, followsCharacterToCity, Ben Covington]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followsCharacterToCity Context triple: [Felicity Porter, followsCharacterToCity, Ben Covington]
-
A.
followsCharacterTo
chosen
Indicates that one character moves after or in pursuit of another character to a particular location or destination.
-
B.
followsCharacter
Indicates that one character moves or acts after another character, maintaining a trailing or subsequent position or sequence relative to them.
-
C.
followsCharacterFrom
Indicates that one character moves or proceeds behind another character, maintaining a trailing or pursuing position relative to them.
-
D.
followsCharacterWhoIs
Indicates that one character consistently trails, pursues, or comes after another character who has a specified property or role.
-
E.
followsCharactersAcross
Indicates that one entity persistently tracks or accompanies specific characters as they move or act across different scenes, locations, or contexts.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9ca5a18819086da68b69eed8cc1 |
completed | April 29, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:16 p.m.