Triple
T18884404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1964 Rameswaram cyclone |
E461921
|
entity |
| Predicate | affectedCountry |
P8692
|
FINISHED |
| Object | India |
—
|
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: India | Statement: [1964 Rameswaram cyclone, affectedCountry, India]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: India Context triple: [1964 Rameswaram cyclone, affectedCountry, India]
-
A.
India
chosen
India is a large South Asian country known for its vast population, cultural and linguistic diversity, and rapid economic growth.
-
B.
India
"India" is a popular song by American rapper Lil Durk, known for its melodic style and romantic themes.
-
C.
Bharata (India)
Bharata (India) is the traditional Sanskrit name for the Indian subcontinent and the Republic of India, rooted in ancient Hindu texts and cultural heritage.
-
D.
Indya
Indya is the given name of Indya Moore, an American actor and model best known for their role on the television series "Pose."
-
E.
Indienne
Indienne was a French naval ship that took part in the early 19th-century Battle of the Basque Roads during the Napoleonic Wars.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d3dcec8190a468162c6a4482f6 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.