Triple
T31783778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Ouko |
E811273
|
entity |
| Predicate | positionHeld |
P8
|
FINISHED |
| Object |
Minister for Industry of Kenya
The Minister for Industry of Kenya is a senior government cabinet position responsible for formulating and implementing national industrialization and manufacturing policies.
|
E1978287
|
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: Minister for Industry of Kenya | Statement: [Robert Ouko, positionHeld, Minister for Industry of Kenya]
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: Minister for Industry of Kenya Triple: [Robert Ouko, positionHeld, Minister for Industry of Kenya]
Generated description
The Minister for Industry of Kenya is a senior government cabinet position responsible for formulating and implementing national industrialization and manufacturing policies.
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_69f348e544a48190ab6e700b05f6438c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abe760f88190b01131bf42e4066c |
completed | May 3, 2026, 1:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2d9d5a94488190afa05e1b18512b86 |
completed | June 13, 2026, 6:11 p.m. |
| NEDg | Description generation | batch_6a2d9e5cacf881909256095fa02d95a9 |
completed | June 13, 2026, 6:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2da22dea008190881a4e08691f9645 |
completed | June 13, 2026, 6:32 p.m. |
Created at: April 30, 2026, 11:37 p.m.