Triple
T22667334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lakshmi |
E559825
|
entity |
| Predicate | epithet |
P743
|
FINISHED |
| Object | Kamala |
—
|
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: Kamala | Statement: [Lakshmi, epithet, Kamala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamala Context triple: [Lakshmi, epithet, Kamala]
-
A.
Kamala
Kamala is the given name of Kamala Harris, the 49th vice president of the United States and the first woman, first Black American, and first South Asian American to hold the office.
-
B.
Kamala
chosen
Kamala is a Hindu goddess associated with prosperity and the tantric form of Lakshmi, revered as one of the ten Mahavidyas.
-
C.
Kamala
Kamala is a renowned Marathi play by Vijay Tendulkar that critiques the commodification of women through the story of a journalist who buys a tribal woman to expose human trafficking.
-
D.
Kamala
Kamala is a coastal resort town on the west coast of Thailand’s Phuket Island, known for its beach, tourism, and relaxed atmosphere.
-
E.
Leona Woods
Leona Woods was an American physicist who, as one of the few women on the Manhattan Project, played a key role in the development and operation of the first nuclear reactor.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1781ceca08190ba1309570e81c5af |
completed | April 29, 2026, 3:16 a.m. |
Created at: April 17, 2026, 3:09 p.m.