Triple
T1353123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hafsa bint Umar |
E28926
|
entity |
| Predicate | maritalStatusWithMuhammad |
P20884
|
FINISHED |
| Object | widow at time of marriage |
—
|
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: widow at time of marriage | Statement: [Hafsa bint Umar, maritalStatusWithMuhammad, widow at time of marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maritalStatusWithMuhammad Context triple: [Hafsa bint Umar, maritalStatusWithMuhammad, widow at time of marriage]
-
A.
spouseStatus
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
B.
marital status
chosen
Indicates the legal or social state of a person’s marriage-related relationship, such as being single, married, divorced, or widowed.
-
C.
maritalBasis
Indicates that the relationship or status in question is founded on, justified by, or determined due to a marital relationship between the involved entities.
-
D.
numberOfMarriagesOfSpouse
Indicates the total count of times the referenced spouse has been married.
-
E.
hasSpouseDescribed
Indicates that one entity is described as the spouse of another 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26d0c4481908fddda89242a57b3 |
completed | March 1, 2026, 10:49 p.m. |
| PD | Predicate disambiguation | batch_69a4bef7700c819099b294e8d9320e70 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:56 p.m.