Triple
T15728732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Field |
E381285
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Field |
E74495
|
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: Field | Statement: [Betty Field, familyName, Field]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Field Context triple: [Betty Field, familyName, Field]
-
A.
Field
chosen
Field is a common English surname borne by numerous notable individuals across business, politics, sports, and the arts.
-
B.
Field
Field is a large-scale installation artwork by Antony Gormley consisting of thousands of small, hand-formed clay figures densely arranged to transform and animate architectural space.
-
C.
Campo
Campo is a town located in the South Region of Brazil.
-
D.
Campo
Campo is a civil parish located within the municipality of Reguengos de Monsaraz in Portugal’s Alentejo region.
-
E.
Campo
Campo is a small rural community in southeastern San Diego County, California, known for its historic railroad, military history sites, and proximity to the U.S.–Mexico border.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb4cc0081909efe330339474017 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82faa5508190a28e2a224d4a4a06 |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:46 a.m.