Triple
T18183022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Leger Stakes |
E435337
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
St Leger
St Leger is a prestigious British flat horse race, the oldest of the five English Classics and the final leg of the traditional Triple Crown.
|
E1310944
|
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: St Leger | Statement: [St Leger Stakes, alsoKnownAs, St Leger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: St Leger Context triple: [St Leger Stakes, alsoKnownAs, St Leger]
-
A.
Frankel
Frankel is a legendary, unbeaten British Thoroughbred racehorse widely regarded as one of the greatest racehorses of all time.
-
B.
Frankel
Frankel is the birth surname of American actress and comedian Bea Arthur, best known for her roles in the television series "Maude" and "The Golden Girls."
-
C.
Danehill
Danehill was a champion Irish-bred Thoroughbred racehorse who became one of the most influential and successful stallions in modern breeding history.
-
D.
Man o’ War
Man o’ War was a legendary American Thoroughbred racehorse widely regarded as one of the greatest racehorses of all time.
-
E.
Big Brown
Big Brown is the widely recognized nickname for the global package delivery company UPS, referencing its distinctive brown trucks and uniforms.
- 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: St Leger Triple: [St Leger Stakes, alsoKnownAs, St Leger]
Generated description
St Leger is a prestigious British flat horse race, the oldest of the five English Classics and the final leg of the traditional Triple Crown.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: St Leger Target entity description: St Leger is a prestigious British flat horse race, the oldest of the five English Classics and the final leg of the traditional Triple Crown.
-
A.
Frankel
Frankel is the birth surname of American actress and comedian Bea Arthur, best known for her roles in the television series "Maude" and "The Golden Girls."
-
B.
Frankel
Frankel is a legendary, unbeaten British Thoroughbred racehorse widely regarded as one of the greatest racehorses of all time.
-
C.
Danehill
Danehill was a champion Irish-bred Thoroughbred racehorse who became one of the most influential and successful stallions in modern breeding history.
-
D.
Man o’ War
Man o’ War was a legendary American Thoroughbred racehorse widely regarded as one of the greatest racehorses of all time.
-
E.
Big Brown
Big Brown is the widely recognized nickname for the global package delivery company UPS, referencing its distinctive brown trucks and uniforms.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dffc432c8190af53da5256dc476c |
completed | April 19, 2026, 2 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03980112908190bbc5371ce2b8a028 |
completed | May 12, 2026, 9:13 p.m. |
| NEDg | Description generation | batch_6a0398dd1d50819087d16ad6f09ef063 |
completed | May 12, 2026, 9:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03998369648190ae2e84ba56827473 |
completed | May 12, 2026, 9:20 p.m. |
Created at: April 10, 2026, 10:31 a.m.