Triple
T37179454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aral District |
E921151
|
entity |
| Predicate | governedBy |
P46
|
FINISHED |
| Object |
akimat of Aral District
The akimat of Aral District is the local executive authority responsible for administering government policies, public services, and regional development within Kazakhstan’s Aral District.
|
E2216285
|
NE 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: akimat of Aral District | Statement: [Aral District, governedBy, akimat of Aral District]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: akimat of Aral District Triple: [Aral District, governedBy, akimat of Aral District]
Generated description
The akimat of Aral District is the local executive authority responsible for administering government policies, public services, and regional development within Kazakhstan’s Aral District.
Provenance (5 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_69f76ea250bc819083f28d81de25cd0c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb35f059288190a6a625dd03b31efc |
completed | May 6, 2026, 12:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a402bc777e8819099ad56c989844a59 |
completed | June 27, 2026, 8 p.m. |
| NEDg | Description generation | batch_6a402df5d9ac8190bb269663af7cdf1f |
completed | June 27, 2026, 8:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a403059f0ac8190b576a259876fdb0f |
completed | June 27, 2026, 8:19 p.m. |
Created at: May 3, 2026, 4:15 p.m.