Triple
T2490657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lily |
E52031
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Lola |
E244304
|
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: Lola | Statement: [Lily, relatedName, Lola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola Context triple: [Lily, relatedName, Lola]
-
A.
Lola
chosen
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
B.
Carla
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
-
C.
Lela
Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
-
D.
Lillita
Lillita is the birth name of Lita Grey, the American actress best known for her early silent film work and marriage to Charlie Chaplin.
-
E.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd18fe32081909580c6272a6013c5 |
completed | March 7, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9111ec8190b464da14bc4be11e |
completed | March 9, 2026, 7:29 p.m. |
Created at: March 6, 2026, 9:45 p.m.