Triple
T2241126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Financial Times |
E49396
|
entity |
| Predicate | hasColumn |
P13119
|
FINISHED |
| Object | Lex |
E37228
|
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: Lex | Statement: [Financial Times, hasColumn, Lex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lex Context triple: [Financial Times, hasColumn, Lex]
-
A.
Lex
chosen
Lex is a common shortened form of the given name Alexander, often used as a modern, informal nickname.
-
B.
LEX
LEX is the abbreviation for the Léman Express, a cross-border commuter rail network serving the Greater Geneva region in Switzerland and France.
-
C.
Luc
Luc is the given name of Luc Longley, the Australian former professional basketball player and three-time NBA champion with the Chicago Bulls.
-
D.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
-
E.
Le
Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0be7fb4819081a5f9c46b616bdb |
completed | March 7, 2026, 6:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b0eef98819083bede32490cba7e |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.