Triple
T5419520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucien |
E121212
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Lulu |
E41999
|
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: Lulu | Statement: [Lucien, hasDiminutive, Lulu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu Context triple: [Lucien, hasDiminutive, Lulu]
-
A.
Lulu
chosen
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
B.
Lulu Ferocity
Lulu Ferocity is a central character known for her bold, dynamic presence and fierce, fashion-forward persona in the narrative of "Pose."
-
C.
Lili
Lili is a 1953 musical fantasy film starring Leslie Caron as a naive orphan who joins a carnival and forms a touching bond with a puppeteer.
-
D.
Lili
Lili is the official mascot character created for the 2017 World Aquatics Championships held in Budapest.
-
E.
Lilly Belle
Lilly Belle is a steam locomotive that operates on the Walt Disney World Railroad at the Magic Kingdom theme park in Florida.
- 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_69bd463b58d88190b258261573de9e91 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87e8f1cc81908b997f8a417697c0 |
completed | March 20, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf3ab152248190847686ff42ac810c |
completed | March 22, 2026, 12:41 a.m. |
Created at: March 20, 2026, 2:05 p.m.