Triple
T37483093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hindu–Muslim conflict |
E931459
|
entity |
| Predicate | hasHistoricalEvent |
P2107
|
FINISHED |
| Object |
Delhi riots of 2020
The Delhi riots of 2020 were a deadly outbreak of communal violence in India’s capital, primarily between Hindu and Muslim communities, that erupted amid protests over the Citizenship Amendment Act.
|
E2229281
|
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: Delhi riots of 2020 | Statement: [Hindu–Muslim conflict, hasHistoricalEvent, Delhi riots of 2020]
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: Delhi riots of 2020 Triple: [Hindu–Muslim conflict, hasHistoricalEvent, Delhi riots of 2020]
Generated description
The Delhi riots of 2020 were a deadly outbreak of communal violence in India’s capital, primarily between Hindu and Muslim communities, that erupted amid protests over the Citizenship Amendment Act.
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_69f76ec382248190b47844df596123c6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba3577e888190b4fa6adefe5c54c7 |
completed | May 6, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a408c4201c08190a0eca3a61f453266 |
completed | June 28, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_6a408d9345e081909b4b57e218254858 |
completed | June 28, 2026, 2:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a408e97e47c81909494b24e0e064f6f |
completed | June 28, 2026, 3:01 a.m. |
Created at: May 3, 2026, 4:17 p.m.