Triple
T38387745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reflets dans l’eau |
E899624
|
entity |
| Predicate | numberOfBooksInCollection |
P5481
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Reflets dans l’eau, numberOfBooksInCollection, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBooksInCollection Context triple: [Reflets dans l’eau, numberOfBooksInCollection, 2]
-
A.
numberOfBooks
chosen
Indicates the quantity of books associated with a given entity.
-
B.
bookNumberInCollection
Indicates the specific numerical position assigned to a book within a particular collection.
-
C.
numberOfCompletedBooks
Indicates the total count of books that an entity has finished reading or completing.
-
D.
intendedNumberOfBooks
Indicates the number of books that an agent plans or aims to have, produce, read, or otherwise be associated with, as opposed to the number actually realized.
-
E.
librarySystemSize
Indicates the overall scale or capacity of a library system, such as the number of branches, items, or resources it encompasses.
- 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_69f76e5c9b808190b486523f5c2f817d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.