Triple
T207287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamb of God |
E4636
|
entity |
| Predicate | takesAway |
P10007
|
FINISHED |
| Object | sins of the world |
—
|
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: sins of the world | Statement: [Lamb of God, takesAway, sins of the world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: takesAway Context triple: [Lamb of God, takesAway, sins of the world]
-
A.
foodCustom
Indicates a culturally specific practice, rule, or tradition related to the preparation, serving, or consumption of food.
-
B.
canOrder
Indicates that one entity has the ability or permission to place an order for another entity or item.
-
C.
order
Indicates that one entity requests, arranges, or directs that another entity provide a good, service, or action, typically in a specified sequence or priority.
-
D.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
E.
traditionalOrder
Indicates that entities are arranged or occur according to a customary, historically established sequence or hierarchy.
- F. None of above. chosen
Provenance (4 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b4c7f908190876c1041db52dffc |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25e292fdc8190bfd51d8848f9ed58 |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.