Triple
T2854268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Scissorhands |
E63162
|
entity |
| Predicate | characterTypeOfEdward |
P43665
|
FINISHED |
| Object | artificial man |
—
|
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: artificial man | Statement: [Edward Scissorhands, characterTypeOfEdward, artificial man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTypeOfEdward Context triple: [Edward Scissorhands, characterTypeOfEdward, artificial man]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
eraCharacter
Indicates that a character is associated with, or belongs to, a particular historical or fictional era.
-
D.
judgeCharacter
Indicates evaluating or forming an opinion about another entity’s moral qualities, personality, or overall character.
-
E.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5f21348190a574fa86bc71c76f |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd10aef88190b750aae07e7df4dc |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdee94c2081908e5075e87e70780a |
completed | March 7, 2026, 8:16 a.m. |
Created at: March 6, 2026, 10:02 p.m.