Triple
T6174839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Celestial Hemisphere |
E137792
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Lyra |
E540387
|
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: Lyra | Statement: [Northern Celestial Hemisphere, contains, Lyra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyra Context triple: [Northern Celestial Hemisphere, contains, Lyra]
-
A.
Lyra
chosen
Lyra is a small northern sky constellation best known for containing the bright star Vega and the Ring Nebula (M57).
-
B.
Lyra Belacqua
Lyra Belacqua is a brave, impulsive young girl from Philip Pullman’s His Dark Materials trilogy, known for her destiny-shaping role and her ability to read the alethiometer.
-
C.
Letta
Letta is an Italian surname most prominently associated with political figures such as Gianni Letta and former Prime Minister Enrico Letta.
-
D.
Serein
Serein is a river in central France that flows through the Burgundy region before joining the Yonne River.
-
E.
Rainelle
Rainelle is a small town located in western Greenbrier County, West Virginia, historically tied to the lumber industry and the surrounding Appalachian region.
- 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dc55b5c819084482b735771c9a8 |
completed | March 22, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c141b69c348190a4e530b7380b643f |
completed | March 23, 2026, 1:35 p.m. |
Created at: March 22, 2026, 4:18 p.m.