Triple
T1191850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shannon Sharpe |
E25378
|
entity |
| Predicate | receptionYardsLeaderForTightEnds |
P26201
|
FINISHED |
| Object | 1990s |
—
|
LITERAL FINISHED |
How this triple was built (2 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: 1990s | Statement: [Shannon Sharpe, receptionYardsLeaderForTightEnds, 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receptionYardsLeaderForTightEnds Context triple: [Shannon Sharpe, receptionYardsLeaderForTightEnds, 1990s]
-
A.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
B.
nflRushingTouchdownsLeader
Indicates the player who led all others in the number of rushing touchdowns in a given NFL season or context.
-
C.
nflRushingYardsLeader
Indicates the player who gained the most rushing yards in the NFL over a specified season or time period.
-
D.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
E.
RavensSpecialTeamsTouchdownPlayer
Indicates that the player scored a touchdown for the Ravens specifically on a special teams play (e.g., kick or punt return, blocked kick).
- F. None of above. chosen
Provenance (4 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd74e2c08190b4a48425f94addaa |
completed | March 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bd0ab5f88190bb583fc63b4cc150 |
completed | March 1, 2026, 10:26 p.m. |
Created at: March 1, 2026, 7:45 p.m.