Triple
T29920641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jorge Luke as Ke-Ni-Tay |
E759918
|
entity |
| Predicate | hasAllegianceInFiction |
P174893
|
FINISHED |
| Object | U.S. Army |
—
|
NE NERFINISHED |
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: U.S. Army | Statement: [Jorge Luke as Ke-Ni-Tay, hasAllegianceInFiction, U.S. Army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAllegianceInFiction Context triple: [Jorge Luke as Ke-Ni-Tay, hasAllegianceInFiction, U.S. Army]
-
A.
hasAuthorAllegiance
Indicates that an author is affiliated with, loyal to, or aligned with a particular group, organization, cause, or ideology.
-
B.
namedForAllegiance
Indicates that an entity is named in reference to, or in honor of, a particular allegiance, affiliation, or loyalty.
-
C.
hadAllegiance
Indicates that an entity was loyally committed or formally bound in support or service to another entity, such as a person, group, or cause.
-
D.
alliesInFiction
Indicates that two or more entities are portrayed as allies or cooperative partners within a fictional context or narrative.
-
E.
isFictionalAgentOf
chosen
Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
- 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_69f2246189fc8190996b63ee1f9a2374 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
Created at: April 29, 2026, 6:14 p.m.