Triple
T16315700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 24: Legacy |
E396167
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Teddy Sears |
E452306
|
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: Teddy Sears | Statement: [24: Legacy, starring, Teddy Sears]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teddy Sears Context triple: [24: Legacy, starring, Teddy Sears]
-
A.
Teddy Sears
chosen
Teddy Sears is an American actor known for his television roles in series such as "Masters of Sex," "The Flash," and "American Horror Story."
-
B.
Teddy Bass
Teddy Bass is a ruthless and manipulative London crime boss in the British gangster film "Sexy Beast."
-
C.
Teddy Reig
Teddy Reig was an American jazz record producer best known for his influential work with bebop and swing musicians in the mid-20th century.
-
D.
Teddy Verseti
Teddy Verseti is a hip-hop artist best known for his guest verse on Chrisette Michele’s song “Epiphany (I’m Leaving).”
-
E.
Teddy Stauffer
Teddy Stauffer was a Swiss-born bandleader and musician, prominent in the swing era and later active in the entertainment and hospitality industries.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e288de57cc81908cec93309347c385 |
completed | April 17, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001fa88e8c8190b0423b60389896dc |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:06 a.m.