Triple
T9345426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cory Hardrict |
E224876
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | Creature |
E792990
|
NE 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: Creature | Statement: [Cory Hardrict, workedOn, Creature]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Creature Context triple: [Cory Hardrict, workedOn, Creature]
-
A.
Creature
chosen
"Creature" is a horror film featuring actor Cory Hardrict in a prominent role.
-
B.
Mi-go
Mi-go are extraterrestrial, fungus-like beings from H. P. Lovecraft’s Cthulhu Mythos, known for their grotesque appearance, advanced alien technology, and sinister experiments on human brains.
-
C.
Creatures
Creatures is a Japanese video game and entertainment company best known for its major role in developing and managing the Pokémon franchise, including games, trading cards, and related media.
-
D.
MUTO
MUTO is a giant parasitic kaiju species in the 2014 film "Godzilla," known for feeding on nuclear material and serving as one of Godzilla’s primary adversaries.
-
E.
Elf
Elf is a popular 2003 Christmas comedy film starring Will Ferrell as a human raised by elves who travels to New York City to find his real father.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4f0ce7b881908714ab526d94fa1d |
completed | April 1, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f3c197208190bde7850de266fdf0 |
completed | April 4, 2026, 11:19 a.m. |
Created at: March 30, 2026, 7:41 p.m.