Triple
T21479150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ambaji |
E529940
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Amba Mata |
—
|
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: Amba Mata | Statement: [Ambaji, alsoKnownAs, Amba Mata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amba Mata Context triple: [Ambaji, alsoKnownAs, Amba Mata]
-
A.
Amba
chosen
Amba is a Hindu goddess, widely revered in western India as a fierce yet protective mother deity often worshipped during Navratri.
-
B.
Amba
Amba is a tragic princess from the Indian epic Mahabharata, known for her vow of vengeance against Bhishma that ultimately leads to her rebirth as Shikhandi.
-
C.
Matareya
Matareya is a prominent Thoroughbred racehorse known for her success as a top-level sprinter-miler in the United States.
-
D.
Matareya
Matareya is a district in northeastern Cairo, Egypt, known for its ancient Heliopolis archaeological remains and historic religious sites.
-
E.
Maiana
Maiana is a low-lying coral atoll in the central Pacific nation of Kiribati, known for its traditional village life and vulnerability to sea-level rise.
- 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_69e0c45acc3881908e38d3f28964152b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea1a37a88190845810cbcacbad65 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:20 p.m.