Triple
T1215932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 15th Army Group |
E26105
|
entity |
| Predicate | includedNationality |
P25077
|
FINISHED |
| Object | British |
—
|
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: British | Statement: [15th Army Group, includedNationality, British]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includedNationality Context triple: [15th Army Group, includedNationality, British]
-
A.
appliesToPersonNationality
Indicates that something is relevant or applicable specifically to a person’s nationality.
-
B.
nationalityRepresented
Indicates the country or nation that an entity officially represents, typically in a professional, competitive, or diplomatic capacity.
-
C.
countryOfCitizenship
Indicates the country in which a person or entity holds legal citizenship.
-
D.
definedCitizenship
Indicates that a formal citizenship status has been legally established or specified for an entity.
-
E.
nationalityInStory
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be059c5c8190a200f09442c22334 |
completed | March 1, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69a4bb62a7c08190a79dcb6ff72ac99b |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc6d5b7881908091e40c8695ef53 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:46 p.m.