Triple
T4906624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Devas |
E109926
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object |
Rudra
Rudra is a fierce and storm-associated Vedic deity later identified with the Hindu god Shiva, known for his destructive and healing powers.
|
E487623
|
NE FINISHED |
How this triple was built (4 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: Rudra | Statement: [Devas, associatedWith, Rudra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rudra Context triple: [Devas, associatedWith, Rudra]
-
A.
Indra
Indra is the king of the gods and lord of storms, war, and the heavens in ancient Vedic and Hindu mythology.
-
B.
Vrushabhadri
Vrushabhadri is one of the sacred hills of the Tirumala range associated with the Tirumala Venkateswara Temple in Andhra Pradesh, India.
-
C.
Agni
Agni is the Vedic god of fire, revered as a central deity of sacrifice and a divine messenger between humans and the gods in ancient Indian religion.
-
D.
Daksha
Daksha was a ruler who succeeded King Balitung in the line of Mataram (Medang) monarchs in early medieval Java.
-
E.
Savitrī
Savitrī is a legendary heroine from the Mahābhārata renowned for her unwavering devotion and intelligence, who wins back her husband’s life from the god of death through courage and spiritual resolve.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Rudra Triple: [Devas, associatedWith, Rudra]
Generated description
Rudra is a fierce and storm-associated Vedic deity later identified with the Hindu god Shiva, known for his destructive and healing powers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rudra Target entity description: Rudra is a fierce and storm-associated Vedic deity later identified with the Hindu god Shiva, known for his destructive and healing powers.
-
A.
Indra
Indra is the king of the gods and lord of storms, war, and the heavens in ancient Vedic and Hindu mythology.
-
B.
Vrushabhadri
Vrushabhadri is one of the sacred hills of the Tirumala range associated with the Tirumala Venkateswara Temple in Andhra Pradesh, India.
-
C.
Agni
Agni is the Vedic god of fire, revered as a central deity of sacrifice and a divine messenger between humans and the gods in ancient Indian religion.
-
D.
Daksha
Daksha was a ruler who succeeded King Balitung in the line of Mataram (Medang) monarchs in early medieval Java.
-
E.
Savitrī
Savitrī is a legendary heroine from the Mahābhārata renowned for her unwavering devotion and intelligence, who wins back her husband’s life from the god of death through courage and spiritual resolve.
- F. None of above. chosen
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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e72ed5c819081104c99a398e0af |
completed | March 20, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c44e91881909513a679886cbdef |
completed | March 21, 2026, 1:25 p.m. |
| NEDg | Description generation | batch_69be9cdb00d88190832363821612ee1f |
completed | March 21, 2026, 1:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be9d7e00f88190b2d12e872fadc181 |
completed | March 21, 2026, 1:30 p.m. |
Created at: March 20, 2026, 1:29 p.m.