Triple
T36841763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miami Miracle |
E910426
|
entity |
| Predicate | firstLateralRecipient |
P205077
|
FINISHED |
| Object | DeVante Parker |
E910431
|
NE 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: DeVante Parker | Statement: [Miami Miracle, firstLateralRecipient, DeVante Parker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstLateralRecipient Context triple: [Miami Miracle, firstLateralRecipient, DeVante Parker]
-
A.
secondLateralRecipient
Indicates that an entity serves as the second recipient in a lateral (sideways or peer-level) transfer or relationship.
-
B.
firstRecipientTeam
Indicates the team that initially receives something (such as an item, message, or responsibility) before any others.
-
C.
firstLeg
Indicates that one entity represents the initial segment or starting portion of a multi-part journey, sequence, or process involving another entity.
-
D.
firstRecipientLeague
Indicates the league that was the initial recipient in a transfer, award, or assignment involving an entity.
-
E.
numberOfLaterals
Indicates the count of lateral branches or side elements associated with a given entity or structure.
- 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_69f76e7f65a881908651b702da592b6d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3dde74418881908431623e65ff4f44 |
completed | June 26, 2026, 2:05 a.m. |
| PD | Predicate disambiguation | batch_6a037a0e039481908a4a2666f76c5363 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:13 p.m.