Triple
T31412056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Welltopia Clinic |
E801293
|
entity |
| Predicate | hasFictionalOwnerOrOperator |
P14482
|
FINISHED |
| Object | Welltopia management |
—
|
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: Welltopia management | Statement: [Welltopia Clinic, hasFictionalOwnerOrOperator, Welltopia management]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalOwnerOrOperator Context triple: [Welltopia Clinic, hasFictionalOwnerOrOperator, Welltopia management]
-
A.
hasFictionalProprietor
chosen
Indicates that something is owned, managed, or run by a fictional character or entity within a narrative context.
-
B.
ownedByFictionalCharacter
Indicates that something is possessed or owned by a fictional (not real-world) character.
-
C.
hasFictionalProperty
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
-
D.
hasFictionalSpokesperson
Indicates that an entity is represented or promoted by a spokesperson who is a fictional or imaginary character.
-
E.
hasFictionalDriver
Indicates that an entity (such as a vehicle or object) is associated with a driver who is a fictional or imaginary character.
- 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_69f348c0dd648190bf2fd7642f78eb06 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
Created at: April 30, 2026, 8:39 p.m.