Triple
T4163628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheila |
E84388
|
entity |
| Predicate | hasAlternativeSpelling |
P457
|
FINISHED |
| Object |
Shayla
Shayla is a feminine given name, often considered a modern or variant spelling of names like Sheila or Shayla-related forms.
|
E419349
|
NE FINISHED |
How this triple was built (4 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: Shayla | Statement: [Sheila, hasAlternativeSpelling, Shayla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shayla Context triple: [Sheila, hasAlternativeSpelling, Shayla]
-
A.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
B.
Shanell
Shanell is an American singer-songwriter and dancer best known for her work with Lil Wayne’s Young Money Entertainment label.
-
C.
Rainelle
Rainelle is a small town located in western Greenbrier County, West Virginia, historically tied to the lumber industry and the surrounding Appalachian region.
-
D.
Brenna
Brenna is a Norwegian surname most notably borne by Tonje Brenna, a contemporary Norwegian politician.
-
E.
Bayta Darell
Bayta Darell is a pivotal character in Isaac Asimov's Foundation series, known for her crucial role in thwarting the Mule's conquest in "Foundation and Empire."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shayla Triple: [Sheila, hasAlternativeSpelling, Shayla]
Generated description
Shayla is a feminine given name, often considered a modern or variant spelling of names like Sheila or Shayla-related forms.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shayla Target entity description: Shayla is a feminine given name, often considered a modern or variant spelling of names like Sheila or Shayla-related forms.
-
A.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
B.
Shanell
Shanell is an American singer-songwriter and dancer best known for her work with Lil Wayne’s Young Money Entertainment label.
-
C.
Rainelle
Rainelle is a small town located in western Greenbrier County, West Virginia, historically tied to the lumber industry and the surrounding Appalachian region.
-
D.
Brenna
Brenna is a Norwegian surname most notably borne by Tonje Brenna, a contemporary Norwegian politician.
-
E.
Bayta Darell
Bayta Darell is a pivotal character in Isaac Asimov's Foundation series, known for her crucial role in thwarting the Mule's conquest in "Foundation and Empire."
- F. None of above. chosen
Provenance (5 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_69aed932cab48190b80ffe35f7029ae1 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02a9bf348190b99cecd19fe65779 |
completed | March 9, 2026, 5:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589ec8a60819099647577b7ab9be1 |
completed | March 14, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69b58b22a1d88190a16e1c1e41291f5a |
completed | March 14, 2026, 4:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58b8bfb288190a097942b5b8c366e |
completed | March 14, 2026, 4:23 p.m. |
Created at: March 9, 2026, 3:44 p.m.