Triple
T16200135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utiroa |
E393175
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Tabonibara |
E393173
|
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: Tabonibara | Statement: [Utiroa, hasNearbySettlement, Tabonibara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabonibara Context triple: [Utiroa, hasNearbySettlement, Tabonibara]
-
A.
Tabonibara
chosen
Tabonibara is a village located on the atoll of Butaritari in the island nation of Kiribati in the central Pacific Ocean.
-
B.
Nabitasan
Nabitasan is a barangay (village-level administrative division) of the municipality of Oton in the province of Iloilo, Philippines.
-
C.
Takabisha
Takabisha is a record-breaking steel roller coaster in Japan renowned for its extremely steep drop and intense thrill elements.
-
D.
Tukabai
Tukabai was a wife of the Maratha nobleman Shahaji Bhonsle and a member of the early 17th-century Maratha aristocracy.
-
E.
Batako-san
Batako-san is a supporting character in the Japanese Anpanman series, known as the cheerful assistant who helps bake and care for the bread-headed heroes.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e22709b0d88190b40787e0520d02ab |
completed | April 17, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff1107908190afda091b53317d81 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:03 a.m.