Triple
T648670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alcyone |
E11296
|
entity |
| Predicate | epithet |
P743
|
FINISHED |
| Object | Halcyone |
E22547
|
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: Halcyone | Statement: [Alcyone, epithet, Halcyone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halcyone Context triple: [Alcyone, epithet, Halcyone]
-
A.
Leonidaion
Leonidaion is an ancient guesthouse complex at Olympia in Greece, built in the 4th century BCE to accommodate distinguished visitors during the Olympic Games.
-
B.
Hespere
Hespere is one of the Hesperides, the nymphs of Greek mythology who tended the blissful garden in the far west that contained the golden apples.
-
C.
CASSIOPE
CASSIOPE is a Canadian multi-purpose satellite that combines scientific research of Earth’s upper atmosphere with a commercial communications payload.
-
D.
Leda
chosen
Leda is a figure in Greek mythology, a Spartan queen best known as the mother of Helen of Troy and the Dioscuri after being seduced by Zeus.
-
E.
Theodosia
Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f308f34819094ba28cfc786051e |
completed | March 1, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a64a4d77e0819098cdd416136fd374 |
completed | March 3, 2026, 2:41 a.m. |
Created at: March 1, 2026, 7:36 p.m.