Triple
T18416791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langzhong |
E441913
|
entity |
| Predicate | hasTraditionalStreetGrid |
P12506
|
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: [Langzhong, hasTraditionalStreetGrid, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalStreetGrid Context triple: [Langzhong, hasTraditionalStreetGrid, yes]
-
A.
hasStreetGridFeature
Indicates that a location or area possesses a specific characteristic or element of a street grid layout, such as a particular pattern, structure, or feature of its road network.
-
B.
hasStreetGridPattern
chosen
Indicates that an area’s street layout follows a structured, grid-like pattern of intersecting roads.
-
C.
hasStreetGridContext
Indicates that one entity is situated within, influenced by, or characterized in relation to the street grid pattern or layout of another entity.
-
D.
hasStreetNumberingSystem
Indicates that a location or area uses an organized system for assigning numbers to buildings or addresses along its streets.
-
E.
containsTraditionalCity
Indicates that one entity geographically or administratively includes or encompasses a traditional city within its boundaries.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a284b608190b77c360a72aceb7a |
completed | April 19, 2026, 6:08 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:47 a.m.