Triple
T3477554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Nepal |
E73410
|
entity |
| Predicate | firstFemaleHolderDate |
P48982
|
FINISHED |
| Object | 2015-10-28 |
—
|
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: 2015-10-28 | Statement: [President of Nepal, firstFemaleHolderDate, 2015-10-28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFemaleHolderDate Context triple: [President of Nepal, firstFemaleHolderDate, 2015-10-28]
-
A.
isFirstFemaleHolderOfOffice
Indicates that a person is the first woman ever to hold a particular office or position.
-
B.
earliestKnownHolder
Indicates that the subject is the first known entity in time to have possessed, held, or been assigned the object.
-
C.
hasNotableFirstFemaleWinnerYear
Indicates the year in which the first notable female winner associated with an entity achieved her win.
-
D.
startTimeAsFirstLadyOfCalifornia
Indicates the date and time when an individual first assumed the role of First Lady of California.
-
E.
firstWomanInSpace
Indicates that the subject is the first female individual to have traveled into outer space.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb5b7584819096eadd1c5268eab1 |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb1ecb02881908394f197e31431b4 |
completed | March 8, 2026, 5:29 p.m. |
Created at: March 8, 2026, 3:17 p.m.