Triple
T37323587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | What If?: Serious Scientific Answers to Absurd Hypothetical Questions |
E926542
|
entity |
| Predicate | goodreadsRating |
P119390
|
FINISHED |
| Object | 4.3 |
—
|
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: 4.3 | Statement: [What If?: Serious Scientific Answers to Absurd Hypothetical Questions, goodreadsRating, 4.3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goodreadsRating Context triple: [What If?: Serious Scientific Answers to Absurd Hypothetical Questions, goodreadsRating, 4.3]
-
A.
hasGoodreadsId
Indicates that an entity is associated with a specific identifier used by Goodreads to uniquely reference it in their system.
-
B.
hasGoodreadsPage
Indicates that an entity has a dedicated page or entry on the Goodreads platform.
-
C.
hasGoodreadsWorkId
Indicates that an entity is associated with a specific Goodreads work identifier linking it to a work entry in the Goodreads database.
-
D.
ratingOfWork
chosen
Indicates the evaluative score or assessment assigned to a particular work or creation.
-
E.
rating
Indicates an evaluation relationship where one entity assigns a qualitative or quantitative score or judgment to another entity.
- 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_69f76eb386d88190a8d511aa11540dfc |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb78cbef988190b8f79d946b46e6b2 |
completed | May 6, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9ac5a08190b24ef308963fc52b |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.