Triple
T12486248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anquan Boldin |
E298439
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anquan
Anquan is a former American football wide receiver best known for his productive NFL career with teams including the Arizona Cardinals, Baltimore Ravens, and San Francisco 49ers.
|
E983842
|
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: Anquan | Statement: [Anquan Boldin, givenName, Anquan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anquan Context triple: [Anquan Boldin, givenName, Anquan]
-
A.
Avin
Avin is a village in the municipality of Hannut in the province of Liège, Belgium.
-
B.
Ain
Ain is a department in eastern France known for its diverse landscapes, historic towns, and proximity to both the Alps and the Swiss border.
-
C.
Ain
Ain is the station code for Ainola railway station, a local rail stop in Finland associated with the home of composer Jean Sibelius.
-
D.
Qualley
Qualley is the surname of an American family best known for actress and model Margaret Qualley and her mother, actress Andie MacDowell.
-
E.
Warehorne
Warehorne is a small rural village and civil parish in the Ashford district of Kent, England, known for its historic church and countryside setting.
- 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: Anquan Triple: [Anquan Boldin, givenName, Anquan]
Generated description
Anquan is a former American football wide receiver best known for his productive NFL career with teams including the Arizona Cardinals, Baltimore Ravens, and San Francisco 49ers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anquan Target entity description: Anquan is a former American football wide receiver best known for his productive NFL career with teams including the Arizona Cardinals, Baltimore Ravens, and San Francisco 49ers.
-
A.
Avin
Avin is a village in the municipality of Hannut in the province of Liège, Belgium.
-
B.
Ain
Ain is a department in eastern France known for its diverse landscapes, historic towns, and proximity to both the Alps and the Swiss border.
-
C.
Ain
Ain is the station code for Ainola railway station, a local rail stop in Finland associated with the home of composer Jean Sibelius.
-
D.
Qualley
Qualley is the surname of an American family best known for actress and model Margaret Qualley and her mother, actress Andie MacDowell.
-
E.
Warehorne
Warehorne is a small rural village and civil parish in the Ashford district of Kent, England, known for its historic church and countryside setting.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94de077bc81908b5ff057a1bf2b4f |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f2b5ed481909ead4f5b96d44064 |
completed | May 2, 2026, 6:15 p.m. |
| NEDg | Description generation | batch_69f6401199408190ad2657802afd93c9 |
completed | May 2, 2026, 6:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f64168d23881908daee7d7cba2160d |
completed | May 2, 2026, 6:24 p.m. |
Created at: April 8, 2026, 9:56 p.m.