Triple
T10498443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucy Flucker |
E247603
|
entity |
| Predicate | marriedToMilitaryRank |
P45589
|
FINISHED |
| Object | Continental Army 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: Continental Army general | Statement: [Lucy Flucker, marriedToMilitaryRank, Continental Army general]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToMilitaryRank Context triple: [Lucy Flucker, marriedToMilitaryRank, Continental Army general]
-
A.
marriedToRank
Indicates that one entity is married to another entity who holds a specific rank or position.
-
B.
militarySpouseOf
chosen
Indicates that one person is or was the legally recognized spouse of another person who is serving or has served in the military.
-
C.
spouseCountryOfService
Indicates the country where a person’s spouse is or was serving in an official or professional capacity.
-
D.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
E.
roleDuringSpouseTenure
Indicates that a person held a particular role or position specifically during the period when their spouse was in office or serving in a defined tenure.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5098e45ec8190a02b981a06786909 |
completed | April 7, 2026, 1:41 p.m. |
| PD | Predicate disambiguation | batch_69d4fb8e24ac8190912c9f11b8bd3084 |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:25 p.m.