Triple
T4659197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie H. Wexner |
E102484
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Express |
E437853
|
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: Express | Statement: [Leslie H. Wexner, associatedWith, Express]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Express Context triple: [Leslie H. Wexner, associatedWith, Express]
-
A.
Express
chosen
Express is an American fashion retailer known for its trendy, youth-oriented apparel and accessories sold through mall-based stores and online.
-
B.
Express
"Express" is a popular song from the film and stage musical "Burlesque," known for its sultry style and association with Christina Aguilera’s performance.
-
C.
Emer
Emer is a given name most notably borne by the 18th-century Swiss legal philosopher Emer de Vattel, known for his influential work on international law.
-
D.
Em
Em is a common shortened form of the given name Emma, often used as an informal nickname.
-
E.
Ent
Ent is a surname most notably associated with Uzal G. Ent, a senior officer in the United States Army Air Forces during World War II.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6328387c81909a500e694a739e6e |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaf9564c819094b9340570a7616b |
completed | March 21, 2026, 1:57 a.m. |
Created at: March 20, 2026, 1:15 p.m.