Triple
T16315785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CTU |
E396169
|
entity |
| Predicate | fictionalJurisdiction |
P122658
|
FINISHED |
| Object | domestic counterterrorism |
—
|
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: domestic counterterrorism | Statement: [CTU, fictionalJurisdiction, domestic counterterrorism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalJurisdiction Context triple: [CTU, fictionalJurisdiction, domestic counterterrorism]
-
A.
fictionalCitizenship
Indicates that an entity is recognized as a citizen of a fictional or imaginary polity, realm, or jurisdiction.
-
B.
governedByFictional
Indicates that one entity is under the rule, control, or authority of another entity that is fictional or exists only in an imagined context.
-
C.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
D.
fictionalField
Indicates that the subject is associated with a fictional or imaginary field, domain, or area rather than a real-world one.
-
E.
fictionalSettingRegion
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b1e9988190a1dce9f1ed7031df |
completed | April 17, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69e219fc72c881909d452274e7af8238 |
completed | April 17, 2026, 11:31 a.m. |
| PDg | Predicate description generation | batch_69e21e56e0348190a3d9475360231a70 |
completed | April 17, 2026, 11:49 a.m. |
Created at: April 10, 2026, 5:06 a.m.