Triple
T35705658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Credit Dauphine |
E1031710
|
entity |
| Predicate | hasFictionalEmployees |
P61558
|
FINISHED |
| Object | bank tellers |
—
|
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: bank tellers | Statement: [Credit Dauphine, hasFictionalEmployees, bank tellers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalEmployees Context triple: [Credit Dauphine, hasFictionalEmployees, bank tellers]
-
A.
hasFictionalStaffMember
chosen
Indicates that an entity includes or employs a staff member who is a fictional character.
-
B.
hasFictionalMember
Indicates that a group, organization, or collection includes at least one member that is fictional rather than real.
-
C.
hasFictionalLeadCharacter
Indicates that a creative work features a particular fictional character as its main or leading protagonist.
-
D.
hasFictionalSpokesperson
Indicates that an entity is represented or promoted by a spokesperson who is a fictional or imaginary character.
-
E.
hasFictionalCoStar
Indicates that one entity appears as a co-star alongside another entity within a fictional work or narrative.
- 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_69f76e0d393c8190b6303c64408736db |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffecdcbac4819093b725a7dbe0e61b |
completed | May 10, 2026, 2:26 a.m. |
| PD | Predicate disambiguation | batch_69ffec3633288190adbbd84e277708dc |
completed | May 10, 2026, 2:23 a.m. |
Created at: May 3, 2026, 4:05 p.m.