Triple
T27165561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viscos |
E682772
|
entity |
| Predicate | hasNotableResidentInFiction |
P196490
|
FINISHED |
| Object | Chantal Prym |
—
|
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: Chantal Prym | Statement: [Viscos, hasNotableResidentInFiction, Chantal Prym]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableResidentInFiction Context triple: [Viscos, hasNotableResidentInFiction, Chantal Prym]
-
A.
hasNotableResident
Indicates that an entity is or has been a well-known or distinguished resident of a particular place or location.
-
B.
hasNotableResidentInTimeline
Indicates that an entity has, at some point in its historical timeline, a notable resident associated with it.
-
C.
residentInFiction
Indicates that one entity is a fictional character or element that resides or exists within the fictional setting, world, or universe represented by another entity.
-
D.
notableEmployeeInFiction
Indicates that a person is a particularly prominent or significant employee within a fictional work or universe.
-
E.
residesInFictionalLocation
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
- 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_69eefacf6e788190a75a64399d9e3109 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69fe59d11e9881909d2f33b7c717030e |
completed | May 8, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69fe394fdfbc8190a931926ae3635cbf |
completed | May 8, 2026, 7:28 p.m. |
| PDg | Predicate description generation | batch_69fe59d03a648190bbe846cb5730a477 |
completed | May 8, 2026, 9:46 p.m. |
Created at: April 27, 2026, 9:21 a.m.