Triple
T31007607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ka-tet of the Dark Tower |
E790112
|
entity |
| Predicate | includesNonHumanMember |
P54138
|
FINISHED |
| Object | Oy |
—
|
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: Oy | Statement: [ka-tet of the Dark Tower, includesNonHumanMember, Oy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesNonHumanMember Context triple: [ka-tet of the Dark Tower, includesNonHumanMember, Oy]
-
A.
includeMember
Indicates that one entity contains or has another entity as a member or part of its composition.
-
B.
hasNumberOfHumanMembers
Indicates the relationship that specifies how many human members are associated with a given entity.
-
C.
hasNonClericalMembers
Indicates that an organization or group includes members who are not part of the clergy or formal religious leadership.
-
D.
canIncludeNonMembersFor
Indicates that something is allowed to include or involve entities that are not members in relation to a specified target or context.
-
E.
includesNonHumanCharacters
chosen
Indicates that the subject contains or features characters that are not human, such as animals, aliens, or other non-human entities.
- F. None of above.
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_69f224c73ca48190a1e46cb58ad4045b |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00b8e0a5508190abc5c1e492bed12e |
completed | May 10, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_6a00b8327d048190850af317f60f0f8b |
completed | May 10, 2026, 4:54 p.m. |
Created at: April 29, 2026, 8:57 p.m.