Triple
T32081619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BookMooch |
E819311
|
entity |
| Predicate | pointSpendingMethod |
P121302
|
FINISHED |
| Object | requesting books from other members |
—
|
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: requesting books from other members | Statement: [BookMooch, pointSpendingMethod, requesting books from other members]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointSpendingMethod Context triple: [BookMooch, pointSpendingMethod, requesting books from other members]
-
A.
paymentMethod
Indicates the means or instrument through which a payment is made in a transaction.
-
B.
payType
Indicates the method or category of payment used in a transaction or compensation arrangement.
-
C.
pointsPaying
chosen
Indicates that one entity is paying or settling an obligation to another entity using points (such as reward or loyalty points) as the form of payment.
-
D.
positionOnSpending
Indicates a stance or viewpoint an entity holds regarding levels or patterns of spending, such as how much should be spent and on what.
-
E.
payCategory
Indicates the classification of a payment or compensation into a specific category (such as type, purpose, or pay band) within a payment or payroll context.
- 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_69f348ff8ef88190931c08ba530a36bc |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b580c3e481909f45d1716cde89ad |
completed | May 3, 2026, 2:40 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:24 a.m.