Triple
T7961188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bitbucket |
E184867
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Bamboo |
E493288
|
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: Bamboo | Statement: [Bitbucket, integratesWith, Bamboo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bamboo Context triple: [Bitbucket, integratesWith, Bamboo]
-
A.
Bamboo
Bamboo is a small rural village located in Saint Ann Parish on the northern coast of Jamaica.
-
B.
Bamboo
chosen
Bamboo is a fast-growing, woody grass known for its tall, hollow stems and widespread use in construction, crafts, and as an ornamental plant.
-
C.
Sakao
Sakao is an Oceanic language spoken on the island of Espiritu Santo in Vanuatu, noted for its complex phonology and distinctive sound changes.
-
D.
Makuti
Makuti is a small settlement in northern Zimbabwe that serves as a key junction and rest stop on the main road between Harare, Kariba, and Chirundu.
-
E.
Thốt Nốt
Thốt Nốt is an urban district of Cần Thơ in Vietnam’s Mekong Delta, known for its agricultural landscape and growing urban development.
- 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_69ca8293a2388190aace944d7ed9c0c0 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b8256dc8190a4b73df7aded9097 |
completed | March 31, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe084d9348190b4102fbdfedca297 |
completed | March 31, 2026, 2:56 p.m. |
Created at: March 30, 2026, 5:12 p.m.