Triple
T38390563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Universe |
E899704
|
entity |
| Predicate | plannedToIncludeCharacter |
P194455
|
FINISHED |
| Object | Dr. Henry Jekyll |
—
|
NE NERFINISHED |
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: Dr. Henry Jekyll | Statement: [Dark Universe, plannedToIncludeCharacter, Dr. Henry Jekyll]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plannedToIncludeCharacter Context triple: [Dark Universe, plannedToIncludeCharacter, Dr. Henry Jekyll]
-
A.
addedCharacter
Indicates that one entity has introduced or included a character into another entity, such as a work, text, or narrative.
-
B.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
C.
dedicatedToCharacter
Indicates that something (such as a work, item, or effort) is formally devoted or addressed in honor of a specific character.
-
D.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
E.
collaboratesWithCharacter
Indicates that one character works together with another character toward a shared goal or activity.
- 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_69f76e5c9b808190b486523f5c2f817d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd6f9d600c8190acf495b7fc632e4b |
completed | May 8, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69fd6e98a2948190a9f78c415ad23b8c |
completed | May 8, 2026, 5:03 a.m. |
| PDg | Predicate description generation | batch_69fd6f9a8bd881909983fe8f4cd0ba98 |
completed | May 8, 2026, 5:07 a.m. |
Created at: May 3, 2026, 4:31 p.m.