Triple
T3546698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle Bachelet |
E75012
|
entity |
| Predicate | firstFemaleHolderOfOffice |
P4487
|
FINISHED |
| Object | President of Chile |
—
|
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: President of Chile | Statement: [Michelle Bachelet, firstFemaleHolderOfOffice, President of Chile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFemaleHolderOfOffice Context triple: [Michelle Bachelet, firstFemaleHolderOfOffice, President of Chile]
-
A.
isFirstFemaleHolderOfOffice
chosen
Indicates that a person is the first woman ever to hold a particular office or position.
-
B.
servedAsFirstLadyOfTheUnitedStatesFrom
Indicates that a person held the role of First Lady of the United States during a specified time period.
-
C.
precededAsFirstLadyOfTheUnitedStatesBy
Indicates that one person served as First Lady of the United States immediately before another person.
-
D.
succeededAsFirstLadyOfTheUnitedStatesBy
Indicates that one person ceased serving as First Lady of the United States and was directly followed in that role by another specific person.
-
E.
officeHolderTitleWhenFemale
Indicates the specific title used for a person holding an office when that office holder is female.
- 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_69ad85d33c6c819081d5ac1df13b5680 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbfcf64488190af8452308734028c |
completed | March 8, 2026, 6:28 p.m. |
| PD | Predicate disambiguation | batch_69adae15749881909b847c6ca73c934e |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:20 p.m.