Triple
T5725107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grande-Colar da Ordem do Infante D. Henrique |
E126245
|
entity |
| Predicate | temCaráter |
P662
|
FINISHED |
| Object | não militar |
—
|
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: não militar | Statement: [Grande-Colar da Ordem do Infante D. Henrique, temCaráter, não militar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temCaráter Context triple: [Grande-Colar da Ordem do Infante D. Henrique, temCaráter, não militar]
-
A.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
B.
textCharacter
Indicates that one entity is a character (such as a letter, digit, or symbol) within a piece of text associated with another entity.
-
C.
treatsCharacter
Indicates how one character behaves toward or interacts with another character, especially in terms of care, respect, or mistreatment.
-
D.
characterizedBy
chosen
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
E.
usesCharacter
Indicates that one entity employs, incorporates, or relies on a particular character (such as a symbol, letter, or persona) in its form, function, or representation.
- 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_69c0082f723881908ce8bb13a0c0f8b7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c6488881909bed4a4534d57f70 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:47 p.m.