Triple
T6731237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Oceanic languages |
E153637
|
entity |
| Predicate | includesLanguage |
P2177
|
FINISHED |
| Object | Nafe |
E153870
|
NE 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: Nafe | Statement: [Southern Oceanic languages, includesLanguage, Nafe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nafe Context triple: [Southern Oceanic languages, includesLanguage, Nafe]
-
A.
Nafe
chosen
Nafe is an indigenous Oceanic language spoken in Vanuatu.
-
B.
Nayel
Nayel is a masculine given name, notably borne by Egyptian-American professional show jumping rider Nayel Nassar.
-
C.
Nabeina
Nabeina is a small village located on Tarawa Atoll in the Pacific island nation of Kiribati.
-
D.
Nefza
Nefza is a town in northwestern Tunisia known for its forests, lakes, and proximity to the Mediterranean coast within the Béja Governorate.
-
E.
Naameh
Naameh is a central character in the 2014 biblical epic film "Noah," portrayed as Noah’s devoted wife and partner in facing the coming flood.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16a30888190ae474d90bb71ac49 |
completed | March 27, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70b029960819090de37c99e80ceb9 |
completed | March 27, 2026, 10:56 p.m. |
Created at: March 27, 2026, 2:09 p.m.