Triple
T30761372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramakrishna’s father-in-law |
E783244
|
entity |
| Predicate | son-in-law |
P31445
|
FINISHED |
| Object | Ramakrishna |
—
|
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: Ramakrishna | Statement: [Ramakrishna’s father-in-law, son-in-law, Ramakrishna]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: son-in-law Context triple: [Ramakrishna’s father-in-law, son-in-law, Ramakrishna]
-
A.
sonInLaw
chosen
Indicates that one person is the husband of another person's child.
-
B.
daughterInLaw
Indicates a relationship where one person is the wife of another person's child.
-
C.
grandsonInLaw
Indicates a relationship where one person is the husband of another person's granddaughter.
-
D.
brotherInLaw
Indicates a relationship where one person is the brother of someone's spouse, the spouse of someone's sibling, or the spouse of the sibling of someone's spouse.
-
E.
inLaw
Indicates a familial relationship created through marriage, such as between a spouse and their partner’s relatives or between relatives of two spouses.
- 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_69f224b047f48190b4f5efeb7ee97b37 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68f9cd4288190942f2f313322a1a8 |
completed | May 2, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:39 p.m.