Triple
T27286765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umm al-Fadl Lubaba bint al-Harith |
E688497
|
entity |
| Predicate | hadKunya |
P26979
|
FINISHED |
| Object | Umm al-Fadl |
—
|
NE NERFINISHED |
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: Umm al-Fadl | Statement: [Umm al-Fadl Lubaba bint al-Harith, hadKunya, Umm al-Fadl]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadKunya Context triple: [Umm al-Fadl Lubaba bint al-Harith, hadKunya, Umm al-Fadl]
-
A.
kunya
chosen
Indicates the honorific or respectful name by which a person is addressed or referred to, typically reflecting esteem, modesty, or social standing.
-
B.
hadKontorIn
Indicates that an entity maintained or operated a commercial office or trading post in a specified location.
-
C.
hadFort
Indicates that an entity possessed, controlled, or contained a fort at some time.
-
D.
has
Indicates that one entity possesses, owns, contains, or includes another entity as part of its state or composition.
-
E.
hadNo
Indicates that one entity completely lacked or did not possess another entity, attribute, or relationship.
- 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_69ef355998e08190bdff849e8f33adce |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f627565c688190a8f4f9fd9ce84990 |
completed | May 2, 2026, 4:33 p.m. |
| PD | Predicate disambiguation | batch_69f620e38aec8190bb184edcdbd6da64 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 27, 2026, 11:12 a.m.