Triple
T12391709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sneakers |
E296008
|
entity |
| Predicate | characterRole |
P268
|
FINISHED |
| Object | Liz |
E220458
|
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: Liz | Statement: [Sneakers, characterRole, Liz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liz Context triple: [Sneakers, characterRole, Liz]
-
A.
Liz
chosen
Liz is a common shortened form or nickname for the given name Elizabeth.
-
B.
Lizz
Lizz is a feminine given name, often used as a shortened form of Elizabeth.
-
C.
Liza
Liza is a feminine given name most famously associated with American actress and singer Liza Minnelli.
-
D.
Liza
Liza is a central tragic heroine in Alexander Pushkin’s short story "The Queen of Spades," whose ill-fated love and entanglement with gambling intrigue drive much of the plot.
-
E.
Lizzie
Lizzie is a common English feminine given name, often used as a diminutive of names like Elizabeth or Liza.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fd0bcc48190bb1a59a3aaa6bfdf |
completed | April 10, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b941d208190b75de2083b8b74a4 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:54 p.m.