Triple
T7275937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Indiana |
E163026
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | EAT |
E133154
|
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: EAT | Statement: [Robert Indiana, notableWork, EAT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EAT Context triple: [Robert Indiana, notableWork, EAT]
-
A.
EAT/DIE
chosen
EAT/DIE is a conceptual artwork by American artist Robert Indiana that juxtaposes the words “EAT” and “DIE” to explore themes of consumption, mortality, and American culture.
-
B.
Eater
Eater is a science fiction novel by Gregory Benford that explores humanity’s encounter with a mysterious, sentient black hole-like entity.
-
C.
Bhakna
Bhakna is an Indian surname notably associated with Sohan Singh Bhakna, a prominent early leader of the Ghadar Party in the Indian independence movement.
-
D.
Eat Me
Eat Me is a provocative 1975 experimental film and installation piece by Brazilian artist Lygia Pape that critiques consumer culture, sexuality, and the commodification of the female body.
-
E.
Ate
Ate is a populous district in the eastern part of Lima, Peru, known for its mix of industrial zones, residential areas, and growing commercial activity.
- 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb2f239c819097c1ac4d6de8b0e5 |
completed | March 27, 2026, 8:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db2c76fc81909632c7ee4e54f81c |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:59 p.m.