Triple
T6247762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nabawiyya |
E139762
|
entity |
| Predicate | hasRelationshipTypeWithSaidMahran |
P10690
|
FINISHED |
| Object | estranged spouse |
—
|
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: estranged spouse | Statement: [Nabawiyya, hasRelationshipTypeWithSaidMahran, estranged spouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithSaidMahran Context triple: [Nabawiyya, hasRelationshipTypeWithSaidMahran, estranged spouse]
-
A.
hasMaritalRelationshipType
Indicates the specific type or nature of the marital relationship that exists between two entities.
-
B.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
belongsToMkhare
Indicates that one entity is administratively contained within or assigned to a specific mkhare (region or district).
-
D.
marriageToMuhammadType
Indicates a marital relationship in which the person is (or was) married to Muhammad, specifying that the marriage is to the individual identified as Muhammad.
-
E.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
- 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_69c008b1c5088190ae6de2555fc05ad8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633a9a048190856d5247d3b28a2e |
completed | March 22, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69c056037bf88190a0a3fe7429345d0b |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:23 p.m.