Triple
T36133662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Nixon (second cousin) |
E1045096
|
entity |
| Predicate | relativeTypeWith Richard Nixon |
P204758
|
FINISHED |
| Object | second cousin |
—
|
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 cousin | Statement: [Richard Nixon (second cousin), relativeTypeWith Richard Nixon, second cousin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeTypeWith Richard Nixon Context triple: [Richard Nixon (second cousin), relativeTypeWith Richard Nixon, second cousin]
-
A.
Richard Nixon role
Indicates the specific official position, job, or capacity in which Richard Nixon served or acted.
-
B.
relationshipToTruman
Indicates the specific familial, social, or professional connection that an entity has with Truman.
-
C.
termRelationToPresident
Indicates the nature of a person’s connection or role in relation to a president, such as their position, association, or involvement with that president.
-
D.
relativeTypeToNapoleonBonaparte
Indicates the specific familial relationship that an entity has to Napoleon Bonaparte.
-
E.
relativeTypeToAbdelFattahElSisi
Indicates the specific familial or kinship relationship that one person has to Abdel Fattah el-Sisi.
- F. None of above. chosen
Provenance (4 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_69f76e36a4508190b5bfc8f594272a4c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:08 p.m.