Triple
T17936968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harsha |
E448492
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Harsha |
—
|
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: Harsha | Statement: [Harsha, givenName, Harsha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harsha Context triple: [Harsha, givenName, Harsha]
-
A.
Harsha
chosen
Harsha was a 7th-century Indian emperor who unified much of northern India and became renowned for his patronage of Buddhism, literature, and the arts.
-
B.
Harsha
Harsha is a prominent Indian cricket commentator and journalist known for his insightful analysis and articulate broadcasting style.
-
C.
Haripal
Haripal is a town in West Bengal, India, known for its railway junction and role as a local commercial and agricultural center within Hooghly district.
-
D.
Harish
Harish is the given name of Harish-Chandra, a prominent Indian-American mathematician and physicist known for his foundational work in representation theory.
-
E.
Akrura
Akrura is a revered figure in Hindu mythology, known as a devout Yadava charioteer and ally of Krishna who played a key role in bringing him to Mathura.
- 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_69e4ad924f6c8190a0d676dfa20c9918 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 10:21 a.m.