Triple
T20237709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madra |
E498194
|
entity |
| Predicate | ruler |
P403
|
FINISHED |
| Object | Ashvapati |
—
|
NE NERFINISHED |
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: Ashvapati | Statement: [Madra, ruler, Ashvapati]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashvapati Context triple: [Madra, ruler, Ashvapati]
-
A.
Ashvapati
chosen
Ashvapati is a figure from Hindu tradition known as the father of the Queen of Ayodhya.
-
B.
Bhartṛhari
Bhartṛhari was a 5th-century Indian philosopher and grammarian whose work on language, meaning, and the philosophy of grammar profoundly shaped later Indian thought.
-
C.
Satyakāma Jābāla
Satyakāma Jābāla is a revered Vedic sage known for his exemplary truthfulness and devotion to spiritual knowledge, prominently featured in the Upanishadic tradition.
-
D.
Pāṇini
Pāṇini was an ancient Indian grammarian whose systematic and highly influential treatise, the Aṣṭādhyāyī, laid the foundations of classical Sanskrit grammar and linguistic analysis.
-
E.
Vaishampayana
Vaishampayana is an ancient sage and disciple of Vyasa, best known in Hindu tradition for reciting and transmitting the Mahabharata.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6716b4c148190bf663b8a747fbfa5 |
completed | April 20, 2026, 6:33 p.m. |
Created at: April 11, 2026, 11:40 p.m.