Triple
T9601025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Eaton |
E231849
|
entity |
| Predicate | woreNumberRetired |
P78235
|
FINISHED |
| Object | 53 |
—
|
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: 53 | Statement: [Mark Eaton, woreNumberRetired, 53]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woreNumberRetired Context triple: [Mark Eaton, woreNumberRetired, 53]
-
A.
wearsNumberRetiredBy
Indicates that one entity wears a jersey number that has been officially retired in honor of another entity.
-
B.
hasJerseyNumberRetired
Indicates that an entity has had its jersey number officially retired, typically in recognition of its contributions or achievements.
-
C.
retiredNumber
Indicates that an entity (typically a team or organization) has formally withdrawn a specific number from future use, usually to honor a particular individual or achievement associated with that number.
-
D.
leagueRetiredNumberBy
Indicates that a sports league has officially retired a specific jersey number in honor of a particular person or entity.
-
E.
jerseyNumberRetiredInHisHonor
chosen
Indicates that a person’s jersey number has been officially retired by a team or organization as a tribute to that person’s contributions or legacy.
- 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_69ca8484838c8190b2049199d22fef70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a3934d481908400a63335d644bd |
completed | April 1, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a359788190b24f82399489f7fe |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:07 p.m.