Triple
T36511623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saptakoteshwar Temple |
E899922
|
entity |
| Predicate | hasSanctumShape |
P190527
|
FINISHED |
| Object | square sanctum |
—
|
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: square sanctum | Statement: [Saptakoteshwar Temple, hasSanctumShape, square sanctum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSanctumShape Context triple: [Saptakoteshwar Temple, hasSanctumShape, square sanctum]
-
A.
hasSanctumCount
Indicates the number of sanctums associated with or contained by a given entity.
-
B.
hasSanctumImage
Indicates that an entity is associated with a specific image representing its sanctum or sacred/private space.
-
C.
hasSanctumDedicatedTo
Indicates that one entity possesses or contains a sanctum (a sacred or private space) that is dedicated to another entity.
-
D.
hasSanctuary
Indicates that one entity provides or serves as a place of refuge, protection, or safe haven for another entity.
-
E.
hasMainSanctumOrientation
Indicates the primary directional orientation of a structure’s main sanctum relative to a reference frame (e.g., cardinal directions).
- 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_69f76e5dada881909da2d34bc7a9202a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc7779d248190afdb348a95375443 |
completed | May 7, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
| PDg | Predicate description generation | batch_69fcc73264e08190b0b5917f32226fae |
completed | May 7, 2026, 5:09 p.m. |
Created at: May 3, 2026, 4:10 p.m.