Triple
T58442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. President |
E1156
|
entity |
| Predicate | hasHonorificFunction |
P2097
|
FINISHED |
| Object | shows respect for the office of the president |
—
|
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: shows respect for the office of the president | Statement: [Mr. President, hasHonorificFunction, shows respect for the office of the president]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHonorificFunction Context triple: [Mr. President, hasHonorificFunction, shows respect for the office of the president]
-
A.
hasHonorificPrefix
Indicates that one entity is used as an honorific title or prefix attached to another entity’s name.
-
B.
honorificPrefix
Indicates the formal title or respectful prefix (e.g., "Dr.", "Mr.", "Prof.") used before a person's name to denote status, role, or honor.
-
C.
honorificRank
Indicates that one entity holds a formal title or honorific status in relation to another entity.
-
D.
honorificTitle
chosen
Indicates that one entity serves as a formal honorific or respectful title used to address or refer to another entity.
-
E.
honorificSuffix
Indicates that one entity is a respectful or formal suffix appended to another entity’s name or title.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24c9057348190aa6692eeeae19569 |
completed | Feb. 28, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69a24ac7547c81909bb68f327cdb9158 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.