Triple
T2787940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greek Gendarmerie |
E61855
|
entity |
| Predicate | relationshipToArmy |
P22805
|
FINISHED |
| Object | cooperated with Hellenic Army |
—
|
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: cooperated with Hellenic Army | Statement: [Greek Gendarmerie, relationshipToArmy, cooperated with Hellenic Army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToArmy Context triple: [Greek Gendarmerie, relationshipToArmy, cooperated with Hellenic Army]
-
A.
hasMilitaryAssociation
chosen
Indicates a relationship in which an entity is connected or affiliated with a military organization, activity, or function.
-
B.
militaryRole
Indicates the specific function, position, or duty an entity holds within a military organization or context.
-
C.
hasMilitaryBranch
Indicates that an entity is associated with, served in, or is part of a specific branch of a military organization.
-
D.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
E.
countryOfMilitaryService
Indicates that an entity served or is serving in the armed forces of a specified country.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdeea881481908d759c72798a50fb |
completed | March 7, 2026, 8:16 a.m. |
| PD | Predicate disambiguation | batch_69abdd025c948190a97dd961a9592bac |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.