Triple
T23972374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicken’s Neck |
E604269
|
entity |
| Predicate | threatScenario |
P109184
|
FINISHED |
| Object | potential vulnerability in event of military conflict |
—
|
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: potential vulnerability in event of military conflict | Statement: [Chicken’s Neck, threatScenario, potential vulnerability in event of military conflict]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: threatScenario Context triple: [Chicken’s Neck, threatScenario, potential vulnerability in event of military conflict]
-
A.
threatType
Indicates the specific category or nature of a threat that one entity poses or represents in relation to another.
-
B.
threatCategory
Indicates the classification of a threat according to its type, severity, or nature within a defined risk or security framework.
-
C.
threatContained
Indicates that an identified threat has been successfully neutralized, controlled, or otherwise prevented from causing further harm or escalation.
-
D.
threatContext
chosen
Indicates the situational conditions, factors, or environment in which a threat occurs or is relevant.
-
E.
threatInStory
Indicates that one entity poses or represents a danger, menace, or harmful intent toward another entity within the context of a narrative or story.
- 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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1dcef248190a04718f6f436dcc8 |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:25 p.m.