Triple
T19785561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisa |
E475254
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Lou |
—
|
NE NERFINISHED |
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: Lou | Statement: [Louisa, hasDiminutive, Lou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lou Context triple: [Louisa, hasDiminutive, Lou]
-
A.
Lou
Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
-
B.
Lou
Lou is a supporting character in the romantic drama film "Stuck in Love," involved in the intertwined relationships and personal struggles of a family of writers.
-
C.
Lou
Lou is the protagonist of the film "Love Lies Bleeding," a determined and emotionally complex character whose choices drive the story’s dark, romantic crime narrative.
-
D.
Lou
Lou is a skilled and resourceful partner-in-crime who helps mastermind the heist in the film "Ocean's 8."
-
E.
Lou
Lou is a character in the crime thriller film "Deadfall," involved in the movie’s tense, violent family-centered plot.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65387d3348190a31f9c2f9bc1c6d9 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.