Triple
T2877267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumbhalgarh Fort |
E56906
|
entity |
| Predicate | hasTempleCount |
P43478
|
FINISHED |
| Object | over 300 temples |
—
|
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: over 300 temples | Statement: [Kumbhalgarh Fort, hasTempleCount, over 300 temples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTempleCount Context triple: [Kumbhalgarh Fort, hasTempleCount, over 300 temples]
-
A.
pilgrimageTempleCount
Indicates the number of temples associated with or visited during a particular pilgrimage.
-
B.
hasSubTemple
Indicates that one temple includes or contains another temple as a subordinate or component temple within its structure or organization.
-
C.
hasHistoricTemple
Indicates that an entity possesses, contains, or is associated with a temple of historical significance.
-
D.
hasShrinesIn
Indicates that one entity possesses or maintains shrines that are located within the area or domain of another entity.
-
E.
hasTemplePurpose
Indicates that something (such as a building, site, or structure) is intended, used, or designated for temple-related purposes or functions.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe007329c8190b0bc1851c7307124 |
completed | March 7, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_69abdd142e4c8190b424cb0c5ff40d04 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde2cdcc48190827195d3ae70aa19 |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 10:03 p.m.