Triple
T20499465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ekambareswarar Temple |
E503261
|
entity |
| Predicate | hasNumberOfPrakarams |
P75233
|
FINISHED |
| Object | multiple concentric prakarams |
—
|
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: multiple concentric prakarams | Statement: [Ekambareswarar Temple, hasNumberOfPrakarams, multiple concentric prakarams]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPrakarams Context triple: [Ekambareswarar Temple, hasNumberOfPrakarams, multiple concentric prakarams]
-
A.
hasNumberOfPrakaras
chosen
Indicates the relationship specifying how many prakaras (enclosure layers or surrounding structures) are associated with a given entity.
-
B.
hasNumberOfKandas
Indicates the relationship specifying how many kandas (sections or books) are associated with a given entity.
-
C.
hasNumberOfVarnas
Indicates the relationship that specifies how many varnas (distinct categories or classes) are associated with a given entity.
-
D.
hasPādaCount
Indicates the relationship specifying how many pādas (metrical feet or lines) are associated with a given entity.
-
E.
parameterCount
Indicates the number of parameters associated with a given function, method, or callable entity.
- 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_69e0b4b1e52c8190894281cf7e3283ab |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cc10cd08190915b6c29c6473f77 |
completed | April 20, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69e59fcdf6e08190a604204615dc56e6 |
completed | April 20, 2026, 3:38 a.m. |
Created at: April 16, 2026, 11:35 a.m.