Triple
T9327669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | You're All the World to Me |
E224432
|
entity |
| Predicate | hasNotableSceneLocation |
P3858
|
FINISHED |
| Object | hotel room set |
—
|
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: hotel room set | Statement: [You're All the World to Me, hasNotableSceneLocation, hotel room set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableSceneLocation Context triple: [You're All the World to Me, hasNotableSceneLocation, hotel room set]
-
A.
notableScene
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
B.
notableLocation
chosen
Indicates that a location is especially significant, prominent, or noteworthy in relation to the subject.
-
C.
notableShowLocation
Indicates that a show or performance is notably associated with, took place at, or is best known for occurring in a particular location.
-
D.
hasRegionalScene
Indicates that something possesses or is associated with a specific regional scene, such as a localized cultural, artistic, or social milieu.
-
E.
hasNotableScenicSpot
Indicates that an entity possesses or is associated with a particularly remarkable or well-known scenic location.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37aa78648190b786b50402b15569 |
completed | April 1, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:39 p.m.