Triple
T11485866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irene Rucker Sheridan |
E272274
|
entity |
| Predicate | marriedToMilitaryRankOfSpouse |
P37264
|
FINISHED |
| Object | 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: general | Statement: [Irene Rucker Sheridan, marriedToMilitaryRankOfSpouse, general]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToMilitaryRankOfSpouse Context triple: [Irene Rucker Sheridan, marriedToMilitaryRankOfSpouse, general]
-
A.
militarySpouseOf
Indicates that one person is or was the legally recognized spouse of another person who is serving or has served in the military.
-
B.
spouseOfMilitaryUnit
Indicates that one entity is the spouse or marital partner of a member associated with the specified military unit.
-
C.
spouseCountryOfService
Indicates the country where a person’s spouse is or was serving in an official or professional capacity.
-
D.
marriedToRank
chosen
Indicates that one entity is married to another entity who holds a specific rank or position.
-
E.
spouseOfConflict
Indicates a marital relationship between two parties who are in conflict or dispute with each other.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1ea00c8190b42cdc13a6bc61c3 |
completed | April 10, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69d808736c5c8190899b5b3b2e797f65 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.