Triple
T35787320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vice Mayor of Beijing |
E1034597
|
entity |
| Predicate | typicalPolicyPortfolios |
P178670
|
FINISHED |
| Object | urban planning and construction |
—
|
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: urban planning and construction | Statement: [Vice Mayor of Beijing, typicalPolicyPortfolios, urban planning and construction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPolicyPortfolios Context triple: [Vice Mayor of Beijing, typicalPolicyPortfolios, urban planning and construction]
-
A.
typicalHoldingsType
Indicates the usual or most common category of holdings associated with an entity or account.
-
B.
portfolioWithin
Indicates that one portfolio is contained within, or is a subset of, another portfolio.
-
C.
tipoDePolíticas
chosen
Indicates a classification relationship where specific policies are associated with or assigned to a particular type or category of policies.
-
D.
typicalPortfolio
Indicates that one entity is the standard or representative portfolio associated with another entity (such as a person, account, or organization).
-
E.
diversificationPolicy
Indicates a policy or strategy that governs how resources, investments, or activities are spread across different options to reduce risk or dependence on any single one.
- 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.