Triple
T3139600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanaa Hamri |
E65611
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Elementary
"Elementary" is a modern American television drama that reimagines Sherlock Holmes as a contemporary detective in New York City working alongside Dr. Joan Watson.
|
E332411
|
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: Elementary | Statement: [Sanaa Hamri, notableWork, Elementary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elementary Context triple: [Sanaa Hamri, notableWork, Elementary]
-
A.
Lower School
Lower School is the elementary-level division of the University of Chicago Laboratory Schools, serving young children in their early years of formal education.
-
B.
Basic
Basic is a discount cigarette brand produced and marketed by Philip Morris USA.
-
C.
Little
Little is a common English surname borne by numerous notable individuals across fields such as sports, politics, and the arts.
-
D.
Central Elementary School
Central Elementary School is a public primary school serving students in the village of Wilmette, Illinois.
-
E.
BASIC
BASIC is a family of high-level, beginner-friendly programming languages originally designed to make computer programming more accessible to non-experts.
- 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: Elementary Triple: [Sanaa Hamri, notableWork, Elementary]
Generated description
"Elementary" is a modern American television drama that reimagines Sherlock Holmes as a contemporary detective in New York City working alongside Dr. Joan Watson.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elementary Target entity description: "Elementary" is a modern American television drama that reimagines Sherlock Holmes as a contemporary detective in New York City working alongside Dr. Joan Watson.
-
A.
Lower School
Lower School is the elementary-level division of the University of Chicago Laboratory Schools, serving young children in their early years of formal education.
-
B.
Basic
Basic is a discount cigarette brand produced and marketed by Philip Morris USA.
-
C.
Little
Little is a common English surname borne by numerous notable individuals across fields such as sports, politics, and the arts.
-
D.
Central Elementary School
Central Elementary School is a public primary school serving students in the village of Wilmette, Illinois.
-
E.
BASIC
BASIC is a family of high-level, beginner-friendly programming languages originally designed to make computer programming more accessible to non-experts.
- 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_69ad8582f564819088c27e1f96153938 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada57743e08190a1069c62e32f1bd4 |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b224e11c508190b69c16dfb5313fc2 |
completed | March 12, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69b22534ce008190b3870460b6542a50 |
completed | March 12, 2026, 2:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b22903b428819083af822577861a6b |
completed | March 12, 2026, 2:46 a.m. |
Created at: March 8, 2026, 3:05 p.m.