Triple
T13276367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kare |
E316198
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Galibi |
E619282
|
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: Galibi | Statement: [Kare, hasAlternativeName, Galibi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Galibi Context triple: [Kare, hasAlternativeName, Galibi]
-
A.
Galibi
chosen
Galibi is a coastal village in northeastern Suriname known for its indigenous communities and important sea turtle nesting beaches.
-
B.
Liboi
Liboi is a small Kenyan border town in the arid northeast near Somalia, serving as a local trading and transit point.
-
C.
Tagakaolo
Tagakaolo is an indigenous ethnolinguistic group in the southern Philippines, primarily in parts of Davao and Sarangani, known for its distinct Austronesian language and cultural traditions.
-
D.
Bantumi
Bantumi is a digital version of the traditional Mancala-style board game that was popularized on early Nokia mobile phones.
-
E.
Vangunu
Vangunu is an Oceanic language of the Meso-Melanesian group spoken on Vangunu Island in the Solomon Islands.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99042f56c819082440c89c0adc442 |
completed | April 11, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716cfea308190836eb4892e7c5eb4 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:26 p.m.