Triple
T22845897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marwan ibn al-Hakam |
E566214
|
entity |
| Predicate | relationshipToUthman |
P75792
|
FINISHED |
| Object | cousin and son-in-law |
—
|
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: cousin and son-in-law | Statement: [Marwan ibn al-Hakam, relationshipToUthman, cousin and son-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToUthman Context triple: [Marwan ibn al-Hakam, relationshipToUthman, cousin and son-in-law]
-
A.
relationToUthman
chosen
Indicates the type of relationship or connection an entity has to Uthman.
-
B.
associatedPersonRelationToMuhammad
Indicates the specific type of relationship or connection a person has to Muhammad.
-
C.
relationToKhalidIbnAlWalid
Indicates a relationship or association that an entity has with Khalid ibn al-Walid.
-
D.
relationshipToMuhajirun
Indicates the nature or type of relationship an entity has with the Muhajirun (the early Muslim emigrants from Mecca).
-
E.
relationshipToTimur
Indicates the type or nature of the relationship that one entity has to Timur.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e882b148190985c085bb1a92aae |
completed | April 29, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:36 p.m.