Triple
T6092507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Field of Blood |
E135800
|
entity |
| Predicate | isPurchasedWith |
P43920
|
FINISHED |
| Object | thirty pieces of silver |
—
|
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: thirty pieces of silver | Statement: [Field of Blood, isPurchasedWith, thirty pieces of silver]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isPurchasedWith Context triple: [Field of Blood, isPurchasedWith, thirty pieces of silver]
-
A.
canBePurchasedWith
Indicates that one entity is able to be bought or acquired using another entity as the form of payment.
-
B.
beganWithPurchaseOf
Indicates that an event, process, or relationship started as a result of, or was initiated by, a purchase.
-
C.
hasInAppPurchases
Indicates that an application offers additional content, features, or services that users can buy from within the app itself.
-
D.
maintainsSeparatePurchasesHistories
Indicates that the subject keeps distinct and non-overlapping records of purchase histories for different entities or contexts.
-
E.
isOftenPurchasedAs
chosen
Indicates that one item is frequently bought together with, or in conjunction with, another item.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ac8c7481909fb22cf157be45ce |
completed | March 22, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.