Triple
T33232645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Until September |
E850737
|
entity |
| Predicate | hasLoverNationality |
P196991
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Until September, hasLoverNationality, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoverNationality Context triple: [Until September, hasLoverNationality, French]
-
A.
hasOwnerNationalityStereotype
Indicates that an entity is associated with a stereotype about the nationality of its owner.
-
B.
hasParticipantNationality
Indicates that a participant in an event, activity, or relation has a specific nationality.
-
C.
isByNationality
Indicates that one entity has a specified nationality or originates from the country represented by the other entity.
-
D.
targetNationality
Indicates that one entity has the specified nationality as its intended or designated target.
-
E.
spouse2Nationality
Indicates that the second spouse in a marital relationship has a specified nationality.
- 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_69f349613f988190a1eb75467d167122 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fe72dca2f08190beff17de3d2aada6 |
completed | May 8, 2026, 11:33 p.m. |
| PD | Predicate disambiguation | batch_69fe70bca8d08190b810e1e616ceac44 |
completed | May 8, 2026, 11:24 p.m. |
| PDg | Predicate description generation | batch_69fe72db8810819089e397961b8ead7a |
completed | May 8, 2026, 11:33 p.m. |
Created at: May 1, 2026, 1:31 a.m.