Triple
T16415895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Homer P. Snyder |
E398682
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Homer |
E768490
|
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: Homer | Statement: [Homer P. Snyder, givenName, Homer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Homer Context triple: [Homer P. Snyder, givenName, Homer]
-
A.
Homer
Homer is a small coastal city on Alaska’s Kenai Peninsula known for its scenic Kachemak Bay setting, fishing, and arts community.
-
B.
Homer
Homer is a small town in northeastern Georgia, United States, serving as the county seat of Banks County.
-
C.
Homer
Homer is a common surname of English origin borne by various notable individuals across fields such as literature, sports, and entertainment.
-
D.
Homer
chosen
Homer is the given name of Homer Stillé Cummings, an American lawyer and politician who served as U.S. Attorney General under President Franklin D. Roosevelt.
-
E.
Homer
Homer is the eternally childlike yet centuries-old vampire in the 1987 cult horror film "Near Dark," known for his unsettling combination of a young boy’s body and an adult predator’s mind.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e3287741008190856882b7f34024fc |
completed | April 18, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c6a89988190a44515f1ca08099f |
completed | May 10, 2026, 8:06 a.m. |
Created at: April 10, 2026, 5:09 a.m.