Triple
T9996382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Louie (2016 film character) |
E197211
|
entity |
| Predicate | differenceFromOriginal |
P57180
|
FINISHED |
| Object | depicted as Gigantopithecus instead of orangutan |
—
|
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: depicted as Gigantopithecus instead of orangutan | Statement: [King Louie (2016 film character), differenceFromOriginal, depicted as Gigantopithecus instead of orangutan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: differenceFromOriginal Context triple: [King Louie (2016 film character), differenceFromOriginal, depicted as Gigantopithecus instead of orangutan]
-
A.
differenceFromStates
Indicates that one state or condition is distinct from, or deviates in some way from, another state or condition.
-
B.
differIn
Indicates that two entities are not the same in at least one specified aspect, attribute, or value.
-
C.
differsFromSourceMaterial
chosen
Indicates that something has been altered or deviates in some way from its original or reference source material.
-
D.
hasOriginalVersion
Indicates that one entity is the original or initial version from which another entity is derived or adapted.
-
E.
differenceDescription
Indicates a textual explanation that characterizes how two entities differ from each other.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcb9af6a88190942cc4991bd373c1 |
completed | April 2, 2026, 1:51 a.m. |
| PD | Predicate disambiguation | batch_69cd1da07db88190945bcdab3ca82e71 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:50 p.m.