Triple
T8368290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chương Mỹ District |
E197388
|
entity |
| Predicate | hasUrbanSubdivisions |
P747
|
FINISHED |
| Object | townships |
—
|
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: townships | Statement: [Chương Mỹ District, hasUrbanSubdivisions, townships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanSubdivisions Context triple: [Chương Mỹ District, hasUrbanSubdivisions, townships]
-
A.
hasHigherLevelSubdivision
Indicates that one administrative or organizational unit is contained within and subordinate to a larger, higher-level subdivision.
-
B.
hasSubdivision
chosen
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
C.
citySubdivision
Indicates that one administrative or geographic unit is a smaller subdivision contained within a larger city.
-
D.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
-
E.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
- 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_69ca82f56730819080cec5d991c76f4c |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb808e56fc81908b5d37482f29452d |
completed | March 31, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69cb70cd04b08190ab5f72afd22a7967 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:01 p.m.