Triple
T15553825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hailey Bieber |
E370817
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object | Balmain |
E40457
|
NE 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: Balmain | Statement: [Hailey Bieber, hasModeledFor, Balmain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balmain Context triple: [Hailey Bieber, hasModeledFor, Balmain]
-
A.
Balmain
chosen
Balmain is a French luxury fashion house renowned for its opulent, sharply tailored designs and influential presence on international runways.
-
B.
Balmain
Balmain is a historic inner-west suburb of Sydney, Australia, known for its waterfront location on Sydney Harbour, preserved Victorian architecture, and vibrant pub and café culture.
-
C.
St. Laurent
St. Laurent is the surname of Louis St. Laurent, who served as the 12th prime minister of Canada in the mid-20th century.
-
D.
St. Laurent
St. Laurent is a dark-skinned Austrian red wine grape variety known for producing deeply colored, aromatic wines with cherry and berry flavors, often compared in style to Pinot Noir.
-
E.
Lemaire
Lemaire is a French surname borne by various notable figures in fields such as sports, politics, and the arts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a96c0c88190808f68601a36b506 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c3e67c881909a9fa1e483a364be |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:09 a.m.