Triple
T14437490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatsumode at Sensō-ji |
E358001
|
entity |
| Predicate | estimatedVisitors |
P427
|
FINISHED |
| Object | millions of visitors over the first few days of January |
—
|
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: millions of visitors over the first few days of January | Statement: [Hatsumode at Sensō-ji, estimatedVisitors, millions of visitors over the first few days of January]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedVisitors Context triple: [Hatsumode at Sensō-ji, estimatedVisitors, millions of visitors over the first few days of January]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
C.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
D.
hasVisitorsFrom
Indicates that an entity receives or has received visitors originating from another specified entity or location.
-
E.
typicalVisitorsPerSeason
Indicates the usual number of visitors associated with each season for a given entity or location.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914a45ec81909ab8ccf302047d7f |
completed | April 14, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69de5c3a02fc819097373f97a260cdeb |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:18 a.m.