Triple
T14610443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taylor Kitsch |
E342944
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Taylor |
E63210
|
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: Taylor | Statement: [Taylor Kitsch, givenName, Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taylor Context triple: [Taylor Kitsch, givenName, Taylor]
-
A.
Taylor
chosen
Taylor is a common English surname borne by numerous notable individuals across fields such as politics, arts, sports, and academia.
-
B.
Taylor
"Taylor" is a song featured on the album "On and On," likely recognized as one of its individual tracks.
-
C.
Taylor
Taylor is a suburban city in Wayne County, Michigan, known for its residential communities and proximity to Detroit.
-
D.
Taylor
Taylor is the given name of Taylor Monét Parks, an American singer-songwriter and actress professionally known as Tayla Parx.
-
E.
Tyler
Tyler is the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44f0dd48190a78662b5998a6722 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda91f437c8190ada4d1c3708faedd |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.