Triple
T34566865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lilliput |
E887514
|
entity |
| Predicate | rivalAllegoricalTarget |
P103334
|
FINISHED |
| Object | France (through Blefuscu) |
—
|
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: France (through Blefuscu) | Statement: [Lilliput, rivalAllegoricalTarget, France (through Blefuscu)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rivalAllegoricalTarget Context triple: [Lilliput, rivalAllegoricalTarget, France (through Blefuscu)]
-
A.
rivalPortrayedBy
Indicates that one entity is portrayed or depicted as the rival of another entity by a specific actor or creator.
-
B.
rivalOf
Indicates a relationship in which two entities compete against or oppose each other, often seeking advantage in the same domain or objective.
-
C.
rivalryCharacterizedBy
Indicates a relationship where a rivalry is defined or distinguished by a particular feature, quality, or circumstance.
-
D.
fictionalRivalOf
chosen
Indicates a rivalry relationship that exists between two entities only within a fictional or narrative context.
-
E.
rivalryCharacterization
Indicates a relationship in which two entities are characterized as rivals, typically defined by ongoing competition, opposition, or conflict between them.
- 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_69f349d1a5fc81908557a46875b2f157 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7234bcaa48190ac970759d34e254a |
completed | May 3, 2026, 10:28 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:02 a.m.