Triple
T37762946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Habibullah Kalakani |
E941330
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object |
Bache Saqqao
Bache Saqqao is the nickname of Habibullah Kalakani, the Tajik rebel leader who briefly ruled Afghanistan after overthrowing King Amanullah Khan in 1929.
|
E2241518
|
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: Bache Saqqao | Statement: [Habibullah Kalakani, alternativeName, Bache Saqqao]
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: Bache Saqqao Triple: [Habibullah Kalakani, alternativeName, Bache Saqqao]
Generated description
Bache Saqqao is the nickname of Habibullah Kalakani, the Tajik rebel leader who briefly ruled Afghanistan after overthrowing King Amanullah Khan in 1929.
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_69f76ee3251881909bb4451aad50752b |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaf18085481908c774e8f8bbb9a41 |
completed | May 6, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40e08300ec81909e11c9bab08d5cb2 |
completed | June 28, 2026, 8:51 a.m. |
| NEDg | Description generation | batch_6a40e113a9748190a97d3bb3bf42a626 |
completed | June 28, 2026, 8:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40e1c418d08190b453aab8f9cc2b89 |
completed | June 28, 2026, 8:56 a.m. |
Created at: May 3, 2026, 4:19 p.m.