Triple
T4919599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guanaco |
E110430
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Alpaca |
E43398
|
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: Alpaca | Statement: [Guanaco, relatedTo, Alpaca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alpaca Context triple: [Guanaco, relatedTo, Alpaca]
-
A.
Vicugna pacos
chosen
Vicugna pacos, commonly known as the alpaca, is a domesticated South American camelid prized for its soft, luxurious fiber and often kept in herds in the Andes.
-
B.
Llama
Llama is an open-source large language model family developed by Meta for a wide range of natural language understanding and generation tasks.
-
C.
Vicuña
Vicuña is a small Chilean town in the Elqui Valley, known for its clear skies, observatories, and production of pisco.
-
D.
Guanaco
The guanaco is a wild South American camelid, closely related to the llama, known for its slender build, fine wool, and adaptation to arid and high-altitude environments.
-
E.
Pongo
Pongo is the intelligent and devoted Dalmatian dog who serves as the central canine protagonist in Disney’s "One Hundred and One Dalmatians."
- 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_69bd4413f9908190afcff44d7929cc4c |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6faa78b88190938bee2f81025083 |
completed | March 20, 2026, 4:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6ff4acd88190a308fbac84d3bad9 |
completed | March 21, 2026, 10:16 a.m. |
Created at: March 20, 2026, 1:29 p.m.