Triple
T9577034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Har Ki Pauri |
E231069
|
entity |
| Predicate | pilgrimFootfall |
P89915
|
FINISHED |
| Object | receives large numbers of pilgrims daily |
—
|
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: receives large numbers of pilgrims daily | Statement: [Har Ki Pauri, pilgrimFootfall, receives large numbers of pilgrims daily]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pilgrimFootfall Context triple: [Har Ki Pauri, pilgrimFootfall, receives large numbers of pilgrims daily]
-
A.
pilgrimsFrom
Indicates that one or more pilgrims originate from, or are associated with coming from, a particular place.
-
B.
isClimbedBarefootBySomePilgrims
Indicates that some pilgrims climb the referenced entity without wearing any footwear.
-
C.
servesPilgrimTrafficTo
Indicates that a location or entity functions as a route, hub, or provider facilitating the movement or flow of pilgrim traffic to a specified destination.
-
D.
pilgrimageAssociatedWith
Indicates a relationship where a pilgrimage is connected or related to a particular entity, such as a place, person, event, or religious tradition.
-
E.
pilgrimsPerYearApprox
Indicates an approximate number of pilgrims who travel to a place within a year.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99ad7d108190a0b8c975351ea727 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:05 p.m.