Triple
T4110044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marpessa |
E88549
|
entity |
| Predicate | abductedBy |
P23387
|
FINISHED |
| Object | Idas |
E413995
|
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: Idas | Statement: [Marpessa, abductedBy, Idas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Idas Context triple: [Marpessa, abductedBy, Idas]
-
A.
Idas
chosen
Idas is a figure in Greek mythology known as a heroic but contentious suitor who abducted Marpessa and later became her husband.
-
B.
Igede
Igede is an ethnic group in central Nigeria known for its distinct language, cultural festivals, and presence in and around Benue State.
-
C.
Idanell
Idanell is the birth name of Nellie Connally, the former First Lady of Texas and widow of Governor John Connally, who was riding in the car with President John F. Kennedy during his assassination.
-
D.
Nisaea
Nisaea was the port town and harbor of ancient Megara in Greece, serving as its main maritime outlet on the Saronic Gulf.
-
E.
Dausa
Dausa is a town and district headquarters in the Indian state of Rajasthan, known for its historical forts, stepwells, and proximity to Jaipur.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af01dbe9808190aefdc89e426cc4de |
completed | March 9, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f1f8ca0819090fb2acb3fe03d14 |
completed | March 14, 2026, 3:30 p.m. |
Created at: March 9, 2026, 3:41 p.m.