Triple
T10138643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Comedy |
E226923
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Aspis |
E226927
|
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: Aspis | Statement: [New Comedy, notableWork, Aspis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aspis Context triple: [New Comedy, notableWork, Aspis]
-
A.
Aspis
chosen
Aspis is an ancient Greek comedy by the playwright Menander, known for its exploration of family, inheritance, and social customs in Athenian society.
-
B.
Caliburnus
Caliburnus is the Latinized medieval name for King Arthur’s legendary sword that later evolved into the more widely known form, Excalibur.
-
C.
Armour
Armour is a Scottish surname most famously associated with Jean Armour, the wife of poet Robert Burns.
-
D.
The Spear
The Spear is the nickname of the U.S. Air Force’s 53rd Wing, a unit known for operational testing and evaluation of advanced weapons and systems.
-
E.
Lorica
Lorica is a historic riverside town in northern Colombia’s Córdoba Department, known for its colonial architecture and cultural blend of Arab and Caribbean influences.
- 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_69ca8433ec308190b8b25a6fe359c34c |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cde88344a481909ee833451fab6e58 |
completed | April 2, 2026, 3:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5ee7b6081909f5c08583a619308 |
completed | April 5, 2026, 10:45 p.m. |
Created at: March 30, 2026, 9:06 p.m.