Triple
T14957070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baxter |
E372958
|
entity |
| Predicate | hasNameInWork |
P35337
|
FINISHED |
| Object | Baxter |
E115891
|
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: Baxter | Statement: [Baxter, hasNameInWork, Baxter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baxter Context triple: [Baxter, hasNameInWork, Baxter]
-
A.
Baxter
chosen
Baxter is a surname and given name of English and Scottish origin, historically associated with the occupation of a baker.
-
B.
Baxter
Baxter is Ron Burgundy’s beloved small dog and loyal sidekick in the comedy film "Anchorman: The Legend of Ron Burgundy."
-
C.
Baxter
Baxter is a small community or locality associated with the area of Essa, likely within the municipality of Essa in Ontario, Canada.
-
D.
Baxter
Baxter is a film editor known for working on the dark fantasy horror movie "Horns."
-
E.
Baxter
Baxter is a collaborative industrial robot developed by Rethink Robotics, designed to safely work alongside humans in manufacturing and research environments.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bda691481909c2d89a362782ed8 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:40 a.m.