Triple
T26663542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugenio María de Hostos Law School |
E672116
|
entity |
| Predicate | hasNamingContext |
P34621
|
FINISHED |
| Object | historical reference |
—
|
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Phrase: historical reference | Statement: [Eugenio María de Hostos Law School, hasNamingContext, historical reference]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNamingContext Context triple: [Eugenio María de Hostos Law School, hasNamingContext, historical reference]
-
A.
hasNaming
Indicates that one entity assigns, bears, or is associated with a specific name or designation provided by another entity.
-
B.
namingAuthorityContext
Indicates the authority or governing context under which an entity’s name is formally assigned or recognized.
-
C.
hasNamingLanguageRoot
Indicates that the name of one entity is derived from, or rooted in, the language of another entity.
-
D.
namedInContextOf
chosen
Indicates that an entity is mentioned or identified specifically within a particular context, situation, or frame of reference.
-
E.
hasDualNameContext
Indicates that an entity is associated with two distinct names or naming contexts that are both relevant in the given setting.
- 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_69eecda00a9c8190b2691f4d89db03b6 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f7221dc9a88190bb8194fcc29c42bc |
completed | May 3, 2026, 10:23 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
Created at: April 27, 2026, 3:07 a.m.