Triple
T6093757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sankat Mochan Hanuman Temple |
E135827
|
entity |
| Predicate | peakVisitationDays |
P65535
|
FINISHED |
| Object | Tuesday |
—
|
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: Tuesday | Statement: [Sankat Mochan Hanuman Temple, peakVisitationDays, Tuesday]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakVisitationDays Context triple: [Sankat Mochan Hanuman Temple, peakVisitationDays, Tuesday]
-
A.
peakDays
chosen
Indicates the days on which something reaches its highest or most intense level of activity, usage, or occurrence.
-
B.
peakDayAttendance
Indicates the number of attendees present on the single highest-attendance day within a given period or event.
-
C.
peakDay
Indicates the specific day on which a quantity, activity, or effect reaches its maximum level within a given period.
-
D.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
E.
visitorCount
Indicates the number of visitors associated with a particular entity, context, or time period.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a9516ec819093e94ee8d3244e1b |
completed | March 22, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.