Triple
T13185819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schuyler |
E313847
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Skylar |
E209404
|
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: Skylar | Statement: [Schuyler, hasVariant, Skylar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skylar Context triple: [Schuyler, hasVariant, Skylar]
-
A.
Skylar
chosen
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
B.
Skylar
Skylar is a magical flying creature from the animated series "Elena of Avalor," serving as one of Princess Elena’s loyal and adventurous companions.
-
C.
Skyler
Skyler is a central character from the television series "Breaking Bad," known as Walter White's wife who becomes increasingly entangled in his criminal activities.
-
D.
Kayla
Kayla is a central character in the tech-comedy web series "Hacks," known for her over-the-top personality and chaotic presence in the workplace.
-
E.
Kaylee
Kaylee is a feminine given name, often considered a modern, creative spelling of names like Cailee, Kayleigh, or Kayla.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4b663c8190b0b18f0785f7b57d |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a2e415481908ad1036376f702dc |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:15 p.m.