Triple
T212684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allah |
E4751
|
entity |
| Predicate | relationshipToHumans |
P8492
|
FINISHED |
| Object | creator |
—
|
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: creator | Statement: [Allah, relationshipToHumans, creator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHumans Context triple: [Allah, relationshipToHumans, creator]
-
A.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
B.
moreDistantlyRelatedTo
Indicates that one entity is related to another by a more distant or indirect relationship compared to some closer reference relationship.
-
C.
notableRelative
Indicates that an entity has a relative who is notable or well-known, specifying that familial relationship.
-
D.
belongsToFamily
Indicates that an entity is a member of, or is associated as part of, a specific family group.
-
E.
relationshipToGovernor
Indicates the specific familial, professional, or social relationship that one entity has to a governor.
- F. None of above. chosen
Provenance (4 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c313d108190a65d3e939f961bef |
completed | Feb. 28, 2026, 3:08 a.m. |
| PD | Predicate disambiguation | batch_69a25b509400819093a6c1a1bac861e3 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25c036b54819090a101c4cbdbcff7 |
completed | Feb. 28, 2026, 3:07 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.