Triple
T24204330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Goes to Hollywood |
E600061
|
entity |
| Predicate | basedOnFameFrom |
P155192
|
FINISHED |
| Object | dating reality shows |
—
|
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: dating reality shows | Statement: [New York Goes to Hollywood, basedOnFameFrom, dating reality shows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnFameFrom Context triple: [New York Goes to Hollywood, basedOnFameFrom, dating reality shows]
-
A.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
-
B.
fameStatus
Indicates the level or state of public recognition or renown associated with an entity.
-
C.
famePeak
Indicates the time or point at which an entity reaches its highest level of fame or public recognition.
-
D.
famousAt
Indicates that an entity is widely known or recognized in a particular place, context, or time.
-
E.
starMadeFamous
Indicates that one entity (such as a work, event, or role) is what caused another entity (typically a person) to become widely known or famous.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27ca38c148190ae65cd692567d43d |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c9834064819082024233d9c6f98f |
completed | April 29, 2026, 9:04 a.m. |
Created at: April 17, 2026, 11:37 p.m.