Triple
T34243061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yves Gluant |
E878519
|
entity |
| Predicate | isFictionalCounterpartIn |
P145382
|
FINISHED |
| Object | The Pink Panther film series |
—
|
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: The Pink Panther film series | Statement: [Yves Gluant, isFictionalCounterpartIn, The Pink Panther film series]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalCounterpartIn Context triple: [Yves Gluant, isFictionalCounterpartIn, The Pink Panther film series]
-
A.
fictionalCounterpartIn
chosen
Indicates that one entity serves as a fictional analogue or stand-in for another entity within a specified work or fictional universe.
-
B.
hasRealityCounterpartInFiction
Indicates that a fictional element corresponds to or is based on a real-world counterpart within a work of fiction.
-
C.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
D.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
E.
hasFictionalAlterEgoOf
Indicates that one entity is the fictional alter ego, persona, or alternate identity of 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_69f349b3618481909df955b063f305b2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:56 a.m.