Triple
T16893366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andamanese |
E424231
|
entity |
| Predicate | hasTraditionalTechnology |
P124889
|
FINISHED |
| Object | bow and arrow |
—
|
LITERAL 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: bow and arrow | Statement: [Andamanese, hasTraditionalTechnology, bow and arrow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalTechnology Context triple: [Andamanese, hasTraditionalTechnology, bow and arrow]
-
A.
hasTechnologyTheme
Indicates that something is associated with, centers on, or prominently features technology as a primary theme or subject.
-
B.
hasTechnologyDomain
Indicates that an entity is associated with, operates within, or is relevant to a particular field or domain of technology.
-
C.
laterUsedTechnology
Indicates that one entity adopted or employed a technology after another entity had already used it.
-
D.
hasTechnologicalActivity
Indicates that an entity engages in, performs, or is involved in some form of technological process, operation, or practice.
-
E.
hasLegacy
Indicates that an entity leaves behind a lasting impact, influence, or inheritance that continues to exist or be recognized over time.
- F. None of above. chosen
Provenance (4 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbc6b97c8190b18aca477d6ef647 |
completed | April 18, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69e32b90ec3c819099c51bb7baf2984c |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e32e2c07b081908c8fee9f5507bb9e |
completed | April 18, 2026, 7:09 a.m. |
Created at: April 10, 2026, 5:29 a.m.