Triple
T19637851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baijnath |
E471447
|
entity |
| Predicate | localEconomyInfluencedBy |
P40344
|
FINISHED |
| Object | pilgrims visiting Baijnath Temple |
—
|
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: pilgrims visiting Baijnath Temple | Statement: [Baijnath, localEconomyInfluencedBy, pilgrims visiting Baijnath Temple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localEconomyInfluencedBy Context triple: [Baijnath, localEconomyInfluencedBy, pilgrims visiting Baijnath Temple]
-
A.
localEconomyImpact
chosen
Indicates the effect that an action, event, or entity has on the economic conditions, activities, or performance of a specific local area or community.
-
B.
regionalEconomyActivity
Indicates the type or level of economic activity occurring within a specific geographic region.
-
C.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
D.
stanceOnEconomy
Indicates a subject's expressed position, opinion, or policy view regarding economic issues or economic policy.
-
E.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64107f3fc8190ace6ae67287d280c |
completed | April 20, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69e514e5cb108190ae260e466c447314 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:44 p.m.