Triple
T33802210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jess Crichton |
E866255
|
entity |
| Predicate | meetsOtherCharactersAtLocation |
P174749
|
FINISHED |
| Object | rooftop |
—
|
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: rooftop | Statement: [Jess Crichton, meetsOtherCharactersAtLocation, rooftop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetsOtherCharactersAtLocation Context triple: [Jess Crichton, meetsOtherCharactersAtLocation, rooftop]
-
A.
encountersCharacter
Indicates that one character comes into contact with or meets another character, typically within a particular situation or context.
-
B.
meetsCharacterAtLocation
chosen
Indicates that one character encounters or comes together with another character at a specific location.
-
C.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
D.
mayMeetAt
Indicates that two or more entities are permitted or able to have a meeting or encounter at a specified place or time.
-
E.
featuresCharacterMeetAndGreets
Indicates that the subject offers opportunities for visitors to meet and interact with characters in organized meet-and-greet sessions.
- 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_69f3499057fc81909d862b1309a3bd71 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7051ad6e4819095e82bbd64761803 |
completed | May 3, 2026, 8:19 a.m. |
| PD | Predicate disambiguation | batch_69f700fe24e08190998e2c96fbaaad38 |
completed | May 3, 2026, 8:02 a.m. |
Created at: May 1, 2026, 1:46 a.m.