Triple
T34795360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R v Penguin Books Ltd |
E1003061
|
entity |
| Predicate | concernedWorkTitle |
P24259
|
FINISHED |
| Object | Lady Chatterley’s Lover |
—
|
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: Lady Chatterley’s Lover | Statement: [R v Penguin Books Ltd, concernedWorkTitle, Lady Chatterley’s Lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: concernedWorkTitle Context triple: [R v Penguin Books Ltd, concernedWorkTitle, Lady Chatterley’s Lover]
-
A.
concernsTitle
Indicates that something is about or relates specifically to a particular title (such as a name, designation, or formal heading).
-
B.
workTitleIn
Indicates that one entity is the title of a work (e.g., book, film, artwork) that appears within or is contained in another entity.
-
C.
workTitle
chosen
Indicates the formal title or name of a work (such as a book, artwork, or composition) associated with an entity.
-
D.
basedOnWorkTitle
Indicates that something is derived from, adapted from, or otherwise created based on a work identified by its title.
-
E.
workTitleType
Indicates the specific category or type of a work’s title (e.g., main title, alternative title, translated title) in relation to that work.
- 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 3:59 p.m.