Triple
T15004969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Division of Capricornia |
E377684
|
entity |
| Predicate | safeOrMarginal |
P116325
|
FINISHED |
| Object | marginal |
—
|
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: marginal | Statement: [Division of Capricornia, safeOrMarginal, marginal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safeOrMarginal Context triple: [Division of Capricornia, safeOrMarginal, marginal]
-
A.
hasMarginOfSafety
Indicates that one entity possesses a sufficient buffer or safety allowance relative to another condition, threshold, or risk level to reduce the likelihood or impact of failure or loss.
-
B.
marginOf
Indicates the difference or buffer between two related quantities, values, or boundaries, often expressing how much one exceeds or falls short of another.
-
C.
mayPass
Indicates that one entity is permitted or authorized to move through, cross, or gain access to another entity or location.
-
D.
notableSafety
Indicates that an entity is recognized for having significant safety characteristics, performance, or impact relative to others.
-
E.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7322b5c81909089cbbf816e1436 |
completed | April 15, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69de9a6531a88190acde65199a477350 |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:54 a.m.