Triple
T16260902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niki Lauda |
E394750
|
entity |
| Predicate | teamsRacedFor |
P19695
|
FINISHED |
| Object |
March
March was a British Formula One constructor and racing car manufacturer active primarily in the 1970s and early 1980s.
|
E1201735
|
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: March | Statement: [Niki Lauda, teamsRacedFor, March]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: March Context triple: [Niki Lauda, teamsRacedFor, March]
-
A.
March
March is a fictional family surname most famously associated with the four sisters in Louisa May Alcott’s novel "Little Women."
-
B.
March
"March" is a critically acclaimed graphic memoir trilogy co-written by civil rights leader John Lewis and Andrew Aydin that chronicles Lewis's experiences in the American civil rights movement.
-
C.
March
March is a common surname of Spanish origin borne by various notable individuals, including Cuban revolutionary Aleida March.
-
D.
March
March is a market town in Cambridgeshire, England, known historically as an important railway and river port center in the Fens.
-
E.
March
March is the third month of the Gregorian calendar, marking the transition from winter to spring in the Northern Hemisphere and often associated with seasonal festivals and observances.
- 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: March Triple: [Niki Lauda, teamsRacedFor, March]
Generated description
March was a British Formula One constructor and racing car manufacturer active primarily in the 1970s and early 1980s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: March Target entity description: March was a British Formula One constructor and racing car manufacturer active primarily in the 1970s and early 1980s.
-
A.
March
March is a market town in Cambridgeshire, England, known historically as an important railway and river port center in the Fens.
-
B.
March
March is a fictional family surname most famously associated with the four sisters in Louisa May Alcott’s novel "Little Women."
-
C.
March
March is the third month of the Gregorian calendar, marking the transition from winter to spring in the Northern Hemisphere and often associated with seasonal festivals and observances.
-
D.
March
March is a river in Central Europe that flows through countries including Austria, Slovakia, and the Czech Republic before joining the Danube.
-
E.
March
March is a common surname of Spanish origin borne by various notable individuals, including Cuban revolutionary Aleida March.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c3e5388190942b0237ab5d1f0f |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000eee110c819088d99b80435ab70b |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a000f7e6338819099598bc22d31cd22 |
completed | May 10, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a001025300c819084933d9c6d19fe97 |
completed | May 10, 2026, 4:57 a.m. |
Created at: April 10, 2026, 5:04 a.m.