Triple
T26068221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makasae language |
E657466
|
entity |
| Predicate | primaryStatusInCountry |
P33447
|
FINISHED |
| Object | regional language in Timor-Leste |
—
|
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: regional language in Timor-Leste | Statement: [Makasae language, primaryStatusInCountry, regional language in Timor-Leste]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryStatusInCountry Context triple: [Makasae language, primaryStatusInCountry, regional language in Timor-Leste]
-
A.
primaryStatus
Indicates the main or most important status assigned to an entity among potentially multiple statuses.
-
B.
primaryNationality
Indicates the main national affiliation or citizenship that most strongly characterizes an entity among possibly multiple nationalities.
-
C.
primaryLocationCountry
Indicates the country that serves as the main or primary location associated with the subject.
-
D.
countrySpecificStatus
chosen
Indicates a status or condition that is defined or applied specifically in the context of a particular country.
-
E.
primaryUseCountry
Indicates the country in which something is primarily used or most commonly utilized.
- 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_69ee5bbe539081909efc7f9dd7c1b53c |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
Created at: April 26, 2026, 7:26 p.m.