Triple
T4169683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rector of the University of Lisbon |
E84530
|
entity |
| Predicate | termUsedInLanguage |
P907
|
FINISHED |
| Object | Portuguese |
—
|
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: Portuguese | Statement: [Rector of the University of Lisbon, termUsedInLanguage, Portuguese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termUsedInLanguage Context triple: [Rector of the University of Lisbon, termUsedInLanguage, Portuguese]
-
A.
usedInLanguage
chosen
Indicates that something (such as a word, expression, or symbol) is employed or occurs within a particular language.
-
B.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
C.
termAlsoUsedFor
Indicates that one term is also used to refer to the same or closely related concept as another term.
-
D.
usedTerm
Indicates that one entity employed, referenced, or applied a particular term in some context.
-
E.
officialTermLanguage
Indicates the language in which an official term is formally expressed or defined.
- 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_69aed932cab48190b80ffe35f7029ae1 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02c730b081908b19e6a4aea1549b |
completed | March 9, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69af018fb0948190a9701b2e8e5d9bac |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:44 p.m.