Triple
T17244088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jabriya |
E418575
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Bayan |
E1202327
|
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: Bayan | Statement: [Jabriya, locatedNear, Bayan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayan Context triple: [Jabriya, locatedNear, Bayan]
-
A.
Bayan
chosen
Bayan is a residential suburb and district located within Kuwait's Hawalli Governorate.
-
B.
Bayan
Bayan is a traditional Sasak village in northern Lombok, Indonesia, known for its preserved indigenous culture, historic mosques, and role as a gateway to the Mount Rinjani area.
-
C.
Mabini
Mabini is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
-
D.
Mabini
Mabini is a coastal municipality in the province of Batangas in the Philippines, known for its diving spots and marine biodiversity.
-
E.
Bangu
Bangu is a working-class neighborhood in the West Zone of Rio de Janeiro, Brazil, known for its hot climate, historic textile industry, and the Bangu Atlético Clube football team.
- 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e21bb5c8190ad960f231fe54665 |
completed | April 19, 2026, 1:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170f388608190b709b1c228a7ba29 |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:39 a.m.