Triple
T1800656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Secular Rescue |
E39709
|
entity |
| Predicate | hasTargetRegion |
P14889
|
FINISHED |
| Object | global |
—
|
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: global | Statement: [Secular Rescue, hasTargetRegion, global]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetRegion Context triple: [Secular Rescue, hasTargetRegion, global]
-
A.
hasTarget
chosen
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
B.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
C.
isKeyRegionFor
Indicates that one region plays a central or strategically important role in relation to a specified process, function, or larger area.
-
D.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
-
E.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d514c081908197ac1f7c7d7a88 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.