Triple
T34551919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dong Anh District |
E887091
|
entity |
| Predicate | hasNumberOfTownships |
P197885
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Dong Anh District, hasNumberOfTownships, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTownships Context triple: [Dong Anh District, hasNumberOfTownships, 1]
-
A.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
B.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
C.
hasTownship
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
D.
hasNumberOfPostTowns
Indicates the relationship specifying how many post towns are associated with a given entity.
-
E.
hasNumberOfRuralSettlements
Indicates the quantity of rural settlements associated with or contained within a given entity.
- 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_69f349cff89081908f91e0b064f4833e |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69feb5e66224819083b87c3707a5a5e0 |
completed | May 9, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69feb3bd700c8190991ed200cd3c04db |
completed | May 9, 2026, 4:10 a.m. |
| PDg | Predicate description generation | batch_69feb5e50a7481908d6bff55bd85e06d |
completed | May 9, 2026, 4:19 a.m. |
Created at: May 1, 2026, 2:02 a.m.