Triple
T26198613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pratap Singha |
E655171
|
entity |
| Predicate | titleHolderOf |
P38
|
FINISHED |
| Object |
King of Assam
The King of Assam was the sovereign ruler of the Ahom kingdom in northeastern India, overseeing its political, military, and cultural affairs.
|
E1715989
|
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: King of Assam | Statement: [Pratap Singha, titleHolderOf, King of Assam]
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: King of Assam Triple: [Pratap Singha, titleHolderOf, King of Assam]
Generated description
The King of Assam was the sovereign ruler of the Ahom kingdom in northeastern India, overseeing its political, military, and cultural affairs.
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_69ee5b48236c81908fe385b6afc4f60b |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f60cd8c4608190bdf0cc6142264239 |
completed | May 2, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11858057d08190a1dc0b8898b82653 |
completed | May 23, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_6a11868d16508190ae8827d4b07f0192 |
completed | May 23, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11876410f4819091b6b45abd657f54 |
completed | May 23, 2026, 10:54 a.m. |
Created at: April 26, 2026, 8:47 p.m.