Triple
T20266998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Is There Love in Space? |
E498994
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bamboo |
—
|
NE NERFINISHED |
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: [Is There Love in Space?, hasPart, Bamboo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bamboo Context triple: [Is There Love in Space?, hasPart, 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.
Bambú
"Bambú" is a popular 1980s pop song by Spanish singer Miguel Bosé, known for its catchy melody and romantic lyrics.
-
D.
Bambousse
Bambousse is a peasant farmer character in Émile Zola’s novel "La Faute de l’Abbé Mouret," representing the rough, earthy rural world that contrasts with the protagonist’s spiritual turmoil.
-
E.
Bambou
Bambou is a French singer, actress, and model best known as the longtime companion and muse of musician Serge Gainsbourg in the 1980s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674d001f081908910eedd262ae13a |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:42 p.m.