Triple
T1168569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eli Lilly and Company |
E24856
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object |
Trulicity
Trulicity is a prescription GLP-1 receptor agonist medication used to improve blood sugar control in adults with type 2 diabetes.
|
E133647
|
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: Trulicity | Statement: [Eli Lilly and Company, notableProduct, Trulicity]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trulicity Context triple: [Eli Lilly and Company, notableProduct, Trulicity]
-
A.
Denesuline
Denesuline are a First Nations people of the larger Dene cultural and linguistic group, traditionally inhabiting subarctic regions of northern Canada.
-
B.
Anpezan
Anpezan is a regional dialect of the Ladin language spoken in parts of the Dolomite area of northern Italy.
-
C.
Vumerity
Vumerity is an oral prescription medication used to treat relapsing forms of multiple sclerosis in adults.
-
D.
Darzalex
Darzalex is a monoclonal antibody drug (daratumumab) used primarily in the treatment of multiple myeloma.
-
E.
Lucentis
Lucentis is a prescription anti-VEGF biologic drug used to treat several serious eye diseases, including wet age-related macular degeneration and diabetic macular edema, by inhibiting abnormal blood vessel growth in the retina.
- 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: Trulicity Triple: [Eli Lilly and Company, notableProduct, Trulicity]
Generated description
Trulicity is a prescription GLP-1 receptor agonist medication used to improve blood sugar control in adults with type 2 diabetes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trulicity Target entity description: Trulicity is a prescription GLP-1 receptor agonist medication used to improve blood sugar control in adults with type 2 diabetes.
-
A.
Denesuline
Denesuline are a First Nations people of the larger Dene cultural and linguistic group, traditionally inhabiting subarctic regions of northern Canada.
-
B.
Anpezan
Anpezan is a regional dialect of the Ladin language spoken in parts of the Dolomite area of northern Italy.
-
C.
Vumerity
Vumerity is an oral prescription medication used to treat relapsing forms of multiple sclerosis in adults.
-
D.
Darzalex
Darzalex is a monoclonal antibody drug (daratumumab) used primarily in the treatment of multiple myeloma.
-
E.
Lucentis
Lucentis is a prescription anti-VEGF biologic drug used to treat several serious eye diseases, including wet age-related macular degeneration and diabetic macular edema, by inhibiting abnormal blood vessel growth in the retina.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bccef84481908864e819884af86c |
completed | March 1, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac668562788190ac5f8d081a46b2ee |
completed | March 7, 2026, 5:55 p.m. |
| NEDg | Description generation | batch_69ac67a07f28819096fcd7b767e07a63 |
completed | March 7, 2026, 6 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac6826ef4c81909fc077bfba22b16f |
completed | March 7, 2026, 6:02 p.m. |
Created at: March 1, 2026, 7:45 p.m.