Triple
T10846874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 4*Town |
E256034
|
entity |
| Predicate | fictionalMembersCount |
P96042
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [4*Town, fictionalMembersCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalMembersCount Context triple: [4*Town, fictionalMembersCount, 5]
-
A.
hasFictionalStaffMember
Indicates that an entity includes or employs a staff member who is a fictional character.
-
B.
allegedMembersInLore
Indicates that certain entities are claimed or reported, but not confirmed, to be members within a specified lore or fictional context.
-
C.
partOfFictionalGroup
Indicates that an entity belongs to or is a member of a specific fictional group, organization, or team.
-
D.
hasFictionalFamily
Indicates that an entity is associated with a family that exists only within a fictional or imaginary context.
-
E.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75113bc188190ac78df0c51d95de6 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101c96708190808fef73199e8482 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:20 p.m.