Triple
T14158972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traylor Howard |
E350887
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Traylor
Traylor is the distinctive given name of American actress Traylor Howard, known for her roles in television series such as "Monk" and "Two Guys and a Girl."
|
E1083877
|
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: Traylor | Statement: [Traylor Howard, givenName, Traylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Traylor Context triple: [Traylor Howard, givenName, Traylor]
-
A.
Stevonne
Stevonne is the given first name of former NFL wide receiver and sports analyst Steve Smith Sr.
-
B.
Vonetta
Vonetta is a feminine given name most notably borne by American bobsledder and Olympic gold medalist Vonetta Flowers.
-
C.
Tressie
Tressie is the given name of Tressie McMillan Cottom, an American sociologist, writer, and public intellectual known for her work on inequality, education, and race.
-
D.
Sarah Baylen
Sarah Baylen was the wife of Nobel Prize–winning chemist Herbert C. Brown and a supportive partner throughout his scientific career.
-
E.
Shelbie Bruce
Shelbie Bruce is an American actress best known for her role as the bilingual daughter Cristina Moreno in the film "Spanglish."
- 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: Traylor Triple: [Traylor Howard, givenName, Traylor]
Generated description
Traylor is the distinctive given name of American actress Traylor Howard, known for her roles in television series such as "Monk" and "Two Guys and a Girl."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Traylor Target entity description: Traylor is the distinctive given name of American actress Traylor Howard, known for her roles in television series such as "Monk" and "Two Guys and a Girl."
-
A.
Stevonne
Stevonne is the given first name of former NFL wide receiver and sports analyst Steve Smith Sr.
-
B.
Vonetta
Vonetta is a feminine given name most notably borne by American bobsledder and Olympic gold medalist Vonetta Flowers.
-
C.
Tressie
Tressie is the given name of Tressie McMillan Cottom, an American sociologist, writer, and public intellectual known for her work on inequality, education, and race.
-
D.
Sarah Baylen
Sarah Baylen was the wife of Nobel Prize–winning chemist Herbert C. Brown and a supportive partner throughout his scientific career.
-
E.
Shelbie Bruce
Shelbie Bruce is an American actress best known for her role as the bilingual daughter Cristina Moreno in the film "Spanglish."
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61377de48190a3470d28f0edd34a |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7ef4d80819098d210503f5d22e9 |
completed | May 7, 2026, 8:37 p.m. |
| NEDg | Description generation | batch_69fd02cee5e0819086718893d1621481 |
completed | May 7, 2026, 9:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd063668f4819099d52bee7e7cdc32 |
completed | May 7, 2026, 9:37 p.m. |
Created at: April 10, 2026, 12:58 a.m.