Triple
T262048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo |
E5560
|
entity |
| Predicate | containsAdministrativeUnitType |
P3892
|
FINISHED |
| Object | special ward |
—
|
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: special ward | Statement: [Tokyo, containsAdministrativeUnitType, special ward]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsAdministrativeUnitType Context triple: [Tokyo, containsAdministrativeUnitType, special ward]
-
A.
hasAdministrativeUnit
chosen
Indicates that one entity possesses, contains, or is associated with another entity that functions as its administrative subdivision or governing unit.
-
B.
successorAdministrativeUnit
Indicates that one administrative unit has officially replaced another in its governing or jurisdictional role.
-
C.
organizationTypeGoverned
Indicates that one entity governs or has authoritative control over a specific type or category of organization.
-
D.
hasAffiliationType
Indicates that one entity is connected to another through a specified kind or category of affiliation or association.
-
E.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b6c968c819094fc903a3a377e15 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.