Triple
T33428116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gotanda commercial district |
E856043
|
entity |
| Predicate | hasDaytimeCrowds |
P20734
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Gotanda commercial district, hasDaytimeCrowds, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDaytimeCrowds Context triple: [Gotanda commercial district, hasDaytimeCrowds, yes]
-
A.
hasCrowdLevel
Indicates the degree or intensity of how crowded a place, event, or situation is.
-
B.
hasEventDayCrowdManagement
Indicates that specific crowd management measures or responsibilities are associated with the day on which an event takes place.
-
C.
hasLiveCrowdOutside
Indicates that there is a live, physically present crowd gathered outside a specified location or entity.
-
D.
crowdWas
Indicates that a crowd possessed or exhibited a particular state, quality, or condition.
-
E.
daytimePopulation
chosen
Indicates the number of people present in a given area during daytime hours, typically reflecting where people work or visit rather than where they reside.
- 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_69f349709e7881908c342b4d34f555f4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7051ad6e4819095e82bbd64761803 |
completed | May 3, 2026, 8:19 a.m. |
| PD | Predicate disambiguation | batch_69f700fe24e08190998e2c96fbaaad38 |
completed | May 3, 2026, 8:02 a.m. |
Created at: May 1, 2026, 1:36 a.m.