Triple
T28059695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tehran County |
E709067
|
entity |
| Predicate | hasMetropolitanCharacter |
P153424
|
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: [Tehran County, hasMetropolitanCharacter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetropolitanCharacter Context triple: [Tehran County, hasMetropolitanCharacter, yes]
-
A.
hasMetropolitan
Indicates that an entity is associated with, served by, or located within a specific metropolitan area.
-
B.
isMetropolitanFor
Indicates that one entity serves as the primary metropolitan center or urban hub for another entity (such as a region, area, or service).
-
C.
hasMetropolitanSee
Indicates that one ecclesiastical jurisdiction serves as the metropolitan (primary or overseeing) see in relation to another church territory.
-
D.
hasMetropolitanStructure
chosen
Indicates that an entity possesses or is organized according to a metropolitan-level administrative or urban structural framework.
-
E.
hasMetropolitanCityCode
Indicates that an entity is associated with a specific metropolitan city identified by a standardized code.
- 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_69ef9b6eb6d88190a3fea236eb0f7bed |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: April 27, 2026, 8:38 p.m.