Triple
T35806386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rock Bottom |
E1035110
|
entity |
| Predicate | BookerTVariantName |
P184130
|
FINISHED |
| Object | Book End |
—
|
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: Book End | Statement: [Rock Bottom, BookerTVariantName, Book End]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: BookerTVariantName Context triple: [Rock Bottom, BookerTVariantName, Book End]
-
A.
movieVariantName
Indicates that one movie is known by an alternative or variant name (such as a translated, regional, or re-release title).
-
B.
brandNameVariant
Indicates that one brand name is an alternative or variant form of another brand name, such as a spelling, regional, or stylistic variation.
-
C.
bookAppearance
Indicates that an entity’s visual or physical characteristics as presented in a book are being described or referenced.
-
D.
televisionVersionName
Indicates the specific name or title assigned to a particular version or edition of a television-related work.
-
E.
nicknameOfProduct
Indicates that one term is used as an informal or alternative name (nickname) for a particular product.
- 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_69f76e1762408190b885a8456862e372 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aaabb58c8190bf81673608ecfb6e |
completed | May 3, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f7aa6795f481908940838ee7041ff5 |
completed | May 3, 2026, 8:04 p.m. |
Created at: May 3, 2026, 4:06 p.m.