Triple
T21592509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leviathan |
E532814
|
entity |
| Predicate | roleInJob41 |
P144084
|
FINISHED |
| Object | example of God’s unmatched power |
—
|
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: example of God’s unmatched power | Statement: [Leviathan, roleInJob41, example of God’s unmatched power]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInJob41 Context triple: [Leviathan, roleInJob41, example of God’s unmatched power]
-
A.
roleInExperience
Indicates the specific function, position, or part an entity plays within a particular experience or event.
-
B.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
C.
employedRole
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
D.
roleInIndustry
Indicates the specific function, position, or capacity an entity holds within a particular industry or sector.
-
E.
roleDuringOccupation
Indicates the specific role or position an entity held during a particular occupation or period of control.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadeb56c8190bce79efadf3c644d |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e63384a8fc819084c596bb53a3a1db |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 16, 2026, 6:32 p.m.