Triple
T32739093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clementine Kruczynski |
E837170
|
entity |
| Predicate | meetsCharacterAtLocation |
P174749
|
FINISHED |
| Object | Montauk beach |
—
|
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: Montauk beach | Statement: [Clementine Kruczynski, meetsCharacterAtLocation, Montauk beach]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetsCharacterAtLocation Context triple: [Clementine Kruczynski, meetsCharacterAtLocation, Montauk beach]
-
A.
isCharacterInSetting
Indicates that a particular character appears or exists within a specified setting or environment.
-
B.
hasCharacterPresence
Indicates that a particular character appears or is present within a specified context, such as a scene, work, or medium.
-
C.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
D.
encountersCharacter
Indicates that one character comes into contact with or meets another character, typically within a particular situation or context.
-
E.
screenCharacterBy
Indicates a relationship where one entity evaluates or selects characters according to certain criteria or standards.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c906d54881909573f2d82ecd7ddd |
completed | May 3, 2026, 4:03 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f717a88190a924d614c2c9bca3 |
completed | May 3, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f6c5fa10dc8190b2c06e4c701bd246 |
completed | May 3, 2026, 3:50 a.m. |
Created at: May 1, 2026, 1:12 a.m.