Triple
T17939643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayyavazhi |
E448552
|
entity |
| Predicate | sacredText |
P1184
|
FINISHED |
| Object | Arul Nool |
—
|
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: Arul Nool | Statement: [Ayyavazhi, sacredText, Arul Nool]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arul Nool Context triple: [Ayyavazhi, sacredText, Arul Nool]
-
A.
Arul Nool
chosen
Arul Nool is a secondary holy text of the Ayyavazhi religious tradition, containing teachings, prophecies, and liturgical guidance associated with its founder Ayya Vaikundar.
-
B.
Nedumkandam
Nedumkandam is a high-range town in Kerala, India, known for its cool climate, cardamom plantations, and location along the Munnar–Thekkady route.
-
C.
Subramaniapuram
Subramaniapuram is a critically acclaimed 2008 Tamil period crime drama film known for its realistic portrayal of 1980s Madurai and its influential, gritty filmmaking style.
-
D.
Velaikkaran
Velaikkaran is a 2017 Tamil-language social drama film starring Sivakarthikeyan that focuses on corporate corruption and the struggles of exploited workers.
-
E.
Vetrimaaran
Vetrimaaran is an acclaimed Indian filmmaker and screenwriter known for his gritty, realistic Tamil-language films and multiple National Film Awards.
- 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4ad9533688190bff773c183ed8505 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 10:21 a.m.