Triple
T1540816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Pinkney Hardy MacArthur |
E32859
|
entity |
| Predicate | spouseRank |
P10641
|
FINISHED |
| Object | Lieutenant General |
—
|
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: Lieutenant General | Statement: [Mary Pinkney Hardy MacArthur, spouseRank, Lieutenant General]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseRank Context triple: [Mary Pinkney Hardy MacArthur, spouseRank, Lieutenant General]
-
A.
spouseOrder
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
spouse
Indicates that two entities are married to each other in a legally or socially recognized partnership.
-
C.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
D.
spouseInstanceOf
Indicates that one entity is the specific spouse (marriage partner) instance of another entity.
-
E.
spouseMemberOf
chosen
Indicates that a person’s spouse is a member of a specified group, organization, or entity.
- 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_69a885ed29088190a3c2d5a3d100c16e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa95c1a2948190a2b98469afec1a7d |
completed | March 6, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69a907b2453c8190a41f6b88c8217d1e |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.