Triple
T4005828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl von Martius |
E89522
|
entity |
| Predicate | taxonAuthorAbbreviation |
P33057
|
FINISHED |
| Object |
Mart.
Mart. is the standard botanical author abbreviation for the German botanist and explorer Carl Friedrich Philipp von Martius, known for his extensive work on Brazilian flora.
|
E407525
|
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: Mart. | Statement: [Carl von Martius, taxonAuthorAbbreviation, Mart.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mart. Context triple: [Carl von Martius, taxonAuthorAbbreviation, Mart.]
-
A.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
B.
Marrar
Marrar is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural community and country lifestyle.
-
C.
Martins
Martins is a common Portuguese and Spanish surname, often used as a patronymic meaning "son of Martin."
-
D.
Ma
Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
-
E.
Martin/Martin
Martin/Martin is a structural engineering firm known for designing major projects such as sports stadiums and large commercial structures in the United States.
- 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: Mart. Triple: [Carl von Martius, taxonAuthorAbbreviation, Mart.]
Generated description
Mart. is the standard botanical author abbreviation for the German botanist and explorer Carl Friedrich Philipp von Martius, known for his extensive work on Brazilian flora.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mart. Target entity description: Mart. is the standard botanical author abbreviation for the German botanist and explorer Carl Friedrich Philipp von Martius, known for his extensive work on Brazilian flora.
-
A.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
B.
Marrar
Marrar is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural community and country lifestyle.
-
C.
Martins
Martins is a common Portuguese and Spanish surname, often used as a patronymic meaning "son of Martin."
-
D.
Ma
Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
-
E.
Martin/Martin
Martin/Martin is a structural engineering firm known for designing major projects such as sports stadiums and large commercial structures in the United States.
- 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_69aed9585e788190bec2d39deba3750f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa60c500819084fcba785b2bf801 |
completed | March 9, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c648d3c8190a85e5cdfb20f6044 |
completed | March 14, 2026, 11:54 a.m. |
| NEDg | Description generation | batch_69b54cf3da208190aa844c9ea66354fe |
completed | March 14, 2026, 11:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b55159dc288190a63d5f5164b73bbb |
completed | March 14, 2026, 12:15 p.m. |
Created at: March 9, 2026, 3:34 p.m.