Triple
T7198132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Gladstone |
E168667
|
entity |
| Predicate | spouseTermCount |
P58368
|
FINISHED |
| Object | four |
—
|
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: four | Statement: [Catherine Gladstone, spouseTermCount, four]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseTermCount Context triple: [Catherine Gladstone, spouseTermCount, four]
-
A.
spouseCount
chosen
Indicates the number of spouses an entity has.
-
B.
numberOfMarriagesOfSpouse
Indicates the total count of times the referenced spouse has been married.
-
C.
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.
-
D.
spouseStatus
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
E.
spouseType
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
- 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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92a5d288190955f703470e75bf3 |
completed | March 27, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:52 p.m.