Triple
T4852234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catholic Relief Services |
E108442
|
entity |
| Predicate | CRSRiceBowlType |
P59939
|
FINISHED |
| Object | Lenten faith-in-action program |
—
|
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: Lenten faith-in-action program | Statement: [Catholic Relief Services, CRSRiceBowlType, Lenten faith-in-action program]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CRSRiceBowlType Context triple: [Catholic Relief Services, CRSRiceBowlType, Lenten faith-in-action program]
-
A.
riceType
Indicates the specific variety or classification of rice associated with an entity.
-
B.
includesBowl
Indicates that something contains or has a bowl as one of its components or elements.
-
C.
rindType
Indicates the type or characteristic of the rind associated with an entity.
-
D.
bowlAccess
Indicates that one entity has the ability or permission to access or use a particular bowl.
-
E.
dishType
Indicates the classification of a dish according to its culinary category or role (e.g., appetizer, main course, dessert).
- 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_69bd440a89548190a5f14ba6da6b97dc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2557388190a2d15571bacd24f3 |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6dda5e808190a26ec85e4499d8e4 |
completed | March 20, 2026, 3:55 p.m. |
Created at: March 20, 2026, 1:26 p.m.