Triple
T32281612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Brown (Morocco) |
E824706
|
entity |
| Predicate | servesInFictional |
P131086
|
FINISHED |
| Object | French Foreign Legion |
—
|
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: French Foreign Legion | Statement: [Tom Brown (Morocco), servesInFictional, French Foreign Legion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesInFictional Context triple: [Tom Brown (Morocco), servesInFictional, French Foreign Legion]
-
A.
worksInFictionalContext
chosen
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
B.
livesInFiction
Indicates that one entity exists or resides within the fictional world or narrative setting created by another entity.
-
C.
appearsInFictionAs
Indicates that one entity is depicted or represented as a character, figure, or element within a fictional work or narrative.
-
D.
usedInFictionalWork
Indicates that something (such as a concept, object, or character) appears or is employed within a specific fictional work.
-
E.
basedInFiction
Indicates that an entity is located or primarily situated within a fictional setting, universe, or narrative world.
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff21cbd9108190a52c0ba42004c669 |
completed | May 9, 2026, noon |
| PD | Predicate disambiguation | batch_69ff1faea91881908c626c70bca5100a |
completed | May 9, 2026, 11:51 a.m. |
Created at: May 1, 2026, 12:43 a.m.