Triple
T25877234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Girl on the Train |
E651937
|
entity |
| Predicate | goodreadsRatingApprox |
P157125
|
FINISHED |
| Object | around 4 out of 5 |
—
|
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: around 4 out of 5 | Statement: [The Girl on the Train, goodreadsRatingApprox, around 4 out of 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goodreadsRatingApprox Context triple: [The Girl on the Train, goodreadsRatingApprox, around 4 out of 5]
-
A.
hasGoodreadsId
Indicates that an entity is associated with a specific identifier used by Goodreads to uniquely reference it in their system.
-
B.
hasGoodreadsWorkId
Indicates that an entity is associated with a specific Goodreads work identifier linking it to a work entry in the Goodreads database.
-
C.
ratingOfWork
Indicates the evaluative score or assessment assigned to a particular work or creation.
-
D.
bookMention
Indicates that one entity (such as a text, person, or source) makes reference to or cites a particular book.
-
E.
bookCharacteristic
chosen
Indicates that a particular characteristic, feature, or attribute is associated with a given book.
- 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_69e7ab3ad9d88190841ddcb93ab02e96 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f602e179ec8190aa45a4614673f7fc |
completed | May 2, 2026, 1:57 p.m. |
| PD | Predicate disambiguation | batch_69f4939148dc81908706cec7d85291bc |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 8:13 a.m.