Triple
T20043043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nobody Likes You |
E497477
|
entity |
| Predicate | hasSubsectionRelation |
P37078
|
FINISHED |
| Object | multi-part suite Homecoming |
—
|
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: multi-part suite Homecoming | Statement: [Nobody Likes You, hasSubsectionRelation, multi-part suite Homecoming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubsectionRelation Context triple: [Nobody Likes You, hasSubsectionRelation, multi-part suite Homecoming]
-
A.
hasSubcategoryRelation
Indicates that one category is a more specific subdivision or subset of another broader category.
-
B.
hasChildrenSection
Indicates that an entity includes or is associated with a dedicated section that contains information about its children.
-
C.
hasSectionIn
chosen
Indicates that one entity contains or includes another entity as a section or subdivision within it.
-
D.
hasSectionWith
Indicates that an entity contains or includes a specific section that satisfies certain conditions or characteristics.
-
E.
hasSectionOn
Indicates that one entity (typically a document or resource) contains a dedicated section or part that specifically addresses or discusses another entity or topic.
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662ec9ae0819097032ff50d6215c2 |
completed | April 20, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69e54ce752748190a0a1ffddd0372271 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:37 p.m.