Triple
T38540380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Green Men |
E924816
|
entity |
| Predicate | roleInToyStory3 |
P204165
|
FINISHED |
| Object | operateDeviceToSaveCharactersFromIncinerator |
—
|
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: operateDeviceToSaveCharactersFromIncinerator | Statement: [Little Green Men, roleInToyStory3, operateDeviceToSaveCharactersFromIncinerator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInToyStory3 Context triple: [Little Green Men, roleInToyStory3, operateDeviceToSaveCharactersFromIncinerator]
-
A.
roleInToyStory2
Indicates that an entity has a specific acting or character role in the movie "Toy Story 2."
-
B.
roleInDespicableMe2
Indicates that an entity has a specific role or participation in the movie "Despicable Me 2."
-
C.
roleInKungFuPanda3
Indicates that an entity participated in the movie "Kung Fu Panda 3" in a specific role (such as actor, voice actor, or production role).
-
D.
roleInMonstersInc
Indicates the specific function, position, or part an entity has within the context of the movie “Monsters, Inc.”
-
E.
roleInGoofTroop
Indicates the specific role or function an entity has within the context of the Goof Troop series or franchise.
- F. None of above. chosen
Provenance (4 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_69f76eadeac081909cdfdd0474cb6765 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a033d975d788190aafc4be10d6c5c1c |
completed | May 12, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_6a033cc2668481908cb696e57632a68f |
completed | May 12, 2026, 2:44 p.m. |
| PDg | Predicate description generation | batch_6a033d9690e081909f65653b0dc80e20 |
completed | May 12, 2026, 2:47 p.m. |
Created at: May 3, 2026, 4:32 p.m.