Triple
T19276529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Higashiosaka industrial area |
E482071
|
entity |
| Predicate | typicalCompanySize |
P135405
|
FINISHED |
| Object | small enterprise |
—
|
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: small enterprise | Statement: [Higashiosaka industrial area, typicalCompanySize, small enterprise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCompanySize Context triple: [Higashiosaka industrial area, typicalCompanySize, small enterprise]
-
A.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
-
B.
typicalGroupSizeRange
Indicates the usual minimum and maximum number of individuals that typically occur together in a group for the given entity.
-
C.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
-
D.
staffSize
Indicates the number of staff members associated with an entity.
-
E.
typicalGroupSize
Indicates the usual or characteristic number of individuals that typically form a group in this context.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbbd5f34819086535f28fd880411 |
completed | April 20, 2026, 10:11 a.m. |
| PD | Predicate disambiguation | batch_69e4dd07a7208190afcd51ba1dc87c33 |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4df51ac6c819091ce72b07790ffa6 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 1:29 p.m.