Triple
T2303099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svetambara |
E51775
|
entity |
| Predicate | nunsWear |
P271
|
FINISHED |
| Object | white clothing |
—
|
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: white clothing | Statement: [Svetambara, nunsWear, white clothing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nunsWear Context triple: [Svetambara, nunsWear, white clothing]
-
A.
tookReligiousVowsOn
Indicates that an entity formally committed to religious vows on a specific date or occasion.
-
B.
religiousOrderAssociation
Indicates that an entity is formally connected to, affiliated with, or a member of a particular religious order.
-
C.
wears
chosen
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
D.
religiousOrderSupported
Indicates that one entity provides support—such as resources, endorsement, or maintenance—to a particular religious order.
-
E.
wearingOrder
Indicates the relative sequence in which items are worn on or over one another (e.g., which garment is worn over or under another).
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abcbabf01081908db3b42bc7c60444 |
completed | March 7, 2026, 6:54 a.m. |
| PD | Predicate disambiguation | batch_69abc58ad33c8190b8d68af41b6f5e07 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.