Triple
T9249315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sivakamu Radhakrishnan |
E222279
|
entity |
| Predicate | spouseOrdinalNumberInOffice |
P62258
|
FINISHED |
| Object | second President of India |
—
|
LITERAL FINISHED |
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: second President of India | Statement: [Sivakamu Radhakrishnan, spouseOrdinalNumberInOffice, second President of India]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrdinalNumberInOffice Context triple: [Sivakamu Radhakrishnan, spouseOrdinalNumberInOffice, second President of India]
-
A.
spouseOrdinalNumberAsPresident
chosen
Indicates the numerical order in which a person’s spouse served as president (e.g., first, second, third).
-
B.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
C.
spouseNumberOfTermsInOffice
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
-
D.
spouseLaterOffice
Indicates that one person’s spouse held a particular office or position at a later time than the person in question.
-
E.
spouseOfHonouree
Indicates that one person is the spouse (married partner) of the honouree.
- F. None of above.
Provenance (3 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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd05f7e9848190939f9199d0c1a572 |
completed | April 1, 2026, 11:48 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4e79e48190b3200247f4624867 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:31 p.m.