Triple
T3814121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument Park plaque (Mariano Rivera) |
E84209
|
entity |
| Predicate | honorsNumber |
P52020
|
FINISHED |
| Object | 42 |
—
|
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: 42 | Statement: [Monument Park plaque (Mariano Rivera), honorsNumber, 42]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: honorsNumber Context triple: [Monument Park plaque (Mariano Rivera), honorsNumber, 42]
-
A.
honors
Indicates that one entity shows respect, recognition, or esteem toward another entity, often in a formal or ceremonial way.
-
B.
honorLevel
Indicates the degree or status of respect, distinction, or recognition accorded to an entity relative to others.
-
C.
honorsRole
Indicates that one entity formally recognizes and respects the position, title, or role held by another entity.
-
D.
majorHonourCount
Indicates the number of significant or top-level honors or awards associated with an entity.
-
E.
nameHonors
Indicates that a name is given to honor, commemorate, or pay tribute to a particular entity.
- 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_69aed931f5908190be2c07af66d4df25 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee7482d708190a3ec74745b102a4c |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef14f9bb4819098e64b527b546d74 |
completed | March 9, 2026, 4:11 p.m. |
Created at: March 9, 2026, 3:17 p.m.