Triple
T3606977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1980–81 NBA season |
E76395
|
entity |
| Predicate | assistsLeader |
P21700
|
FINISHED |
| Object |
Johnny Moore
Johnny Moore is a former NBA point guard best known for his playmaking and floor leadership with the San Antonio Spurs in the early 1980s.
|
E372709
|
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: Johnny Moore | Statement: [1980–81 NBA season, assistsLeader, Johnny Moore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johnny Moore Context triple: [1980–81 NBA season, assistsLeader, Johnny Moore]
-
A.
Dan Moore
Dan Moore is a fictional character appearing in the work "Cane."
-
B.
Clifton James
Clifton James was an American character actor best known for his comic portrayals of Southern lawmen in films such as the James Bond movies Live and Let Die and The Man with the Golden Gun.
-
C.
Victor Moore
Victor Moore was an American stage and film actor and comedian best known for his character roles in early 20th-century Hollywood musicals and comedies.
-
D.
Nick Moore
Nick Moore is a British film editor best known for his work on popular films such as "Love Actually."
-
E.
Ben Moore
Ben Moore is a British composer and musician recognized for his contributions to contemporary classical and choral music.
- 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: Johnny Moore Triple: [1980–81 NBA season, assistsLeader, Johnny Moore]
Generated description
Johnny Moore is a former NBA point guard best known for his playmaking and floor leadership with the San Antonio Spurs in the early 1980s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Johnny Moore Target entity description: Johnny Moore is a former NBA point guard best known for his playmaking and floor leadership with the San Antonio Spurs in the early 1980s.
-
A.
Dan Moore
Dan Moore is a fictional character appearing in the work "Cane."
-
B.
Clifton James
Clifton James was an American character actor best known for his comic portrayals of Southern lawmen in films such as the James Bond movies Live and Let Die and The Man with the Golden Gun.
-
C.
Victor Moore
Victor Moore was an American stage and film actor and comedian best known for his character roles in early 20th-century Hollywood musicals and comedies.
-
D.
Nick Moore
Nick Moore is a British film editor best known for his work on popular films such as "Love Actually."
-
E.
Ben Moore
Ben Moore is a British composer and musician recognized for his contributions to contemporary classical and choral music.
- 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc1e33cfc8190afc716b19480fbce |
completed | March 8, 2026, 6:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4330bc9f081909a5c1e6da885cfc5 |
completed | March 13, 2026, 3:53 p.m. |
| NEDg | Description generation | batch_69b4364e9f848190ab1e5e03c43dfae4 |
completed | March 13, 2026, 4:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4367bec908190b0c6dc844af5e366 |
completed | March 13, 2026, 4:08 p.m. |
Created at: March 8, 2026, 3:22 p.m.