Triple
T36476634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jayson Megna |
E898683
|
entity |
| Predicate | hasWornJerseyNumber |
P2651
|
FINISHED |
| Object | 46 (Pittsburgh Penguins) |
—
|
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: 46 (Pittsburgh Penguins) | Statement: [Jayson Megna, hasWornJerseyNumber, 46 (Pittsburgh Penguins)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWornJerseyNumber Context triple: [Jayson Megna, hasWornJerseyNumber, 46 (Pittsburgh Penguins)]
-
A.
hasJersey
Indicates that one entity possesses or is associated with a particular jersey.
-
B.
hasJerseyName
Indicates that an entity is associated with a specific name or label displayed on its jersey.
-
C.
hasJerseyNumberRetired
Indicates that an entity has had its jersey number officially retired, typically in recognition of its contributions or achievements.
-
D.
hasJerseyColor
Indicates that an entity’s jersey possesses or is characterized by a specific color.
-
E.
jerseyNumber
chosen
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
- F. None of above.
Provenance (3 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_69f76e5a0e088190a2b6706aeb41723c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fec5c503808190bde0b1829ea43071 |
completed | May 9, 2026, 5:27 a.m. |
| PD | Predicate disambiguation | batch_69fec535dd6c8190a6ab85201f5a04a9 |
completed | May 9, 2026, 5:25 a.m. |
Created at: May 3, 2026, 4:10 p.m.