Triple
T2464904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Euphemia de Ross |
E55223
|
entity |
| Predicate | spouseOrderWithJohnRandolph |
P4764
|
FINISHED |
| Object | first husband |
—
|
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: first husband | Statement: [Euphemia de Ross, spouseOrderWithJohnRandolph, first husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrderWithJohnRandolph Context triple: [Euphemia de Ross, spouseOrderWithJohnRandolph, first husband]
-
A.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
spouseOfHonouree
Indicates that one person is the spouse (married partner) of the honouree.
-
C.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
D.
marriedToRank
Indicates that one entity is married to another entity who holds a specific rank or position.
-
E.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0b3ea308190a6d8499c2a542c50 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.