Triple
T15368275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chain Letter (2010 film) |
E367471
|
entity |
| Predicate | hasKiller |
P118301
|
FINISHED |
| Object | chain letter killer |
—
|
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: chain letter killer | Statement: [Chain Letter (2010 film), hasKiller, chain letter killer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKiller Context triple: [Chain Letter (2010 film), hasKiller, chain letter killer]
-
A.
hasKillerType
Indicates a relationship where an entity is associated with a specific type or category of killer.
-
B.
hasKillerDoll
Indicates that an entity possesses, is associated with, or is responsible for a doll characterized as a killer.
-
C.
killedBy
Indicates that one entity caused the death of another entity.
-
D.
hasSerialKiller
Indicates that one entity is a serial killer associated with, responsible for, or targeting another entity.
-
E.
allegedToHaveKilled
Indicates that one entity is claimed or accused, but not proven, to have killed another entity.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4a7cdc8190b7b48c97e774c306 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca9ab7e88190a9261ef27be665b1 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:18 a.m.