Triple
T3988882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain Byron Hadley |
E86938
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hadley |
E254496
|
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: Hadley | Statement: [Captain Byron Hadley, familyName, Hadley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadley Context triple: [Captain Byron Hadley, familyName, Hadley]
-
A.
Hadley
chosen
Hadley is a given name most notably borne by Hadley Richardson, the first wife of writer Ernest Hemingway.
-
B.
Tuthill
Tuthill is a surname most notably associated with William Burnet Tuthill, the American architect who designed Carnegie Hall in New York City.
-
C.
Aurora
Aurora is a wealthy, technologically advanced Spacer world in Isaac Asimov’s Robot series, known for its robot-dependent society and pivotal role in the development of human-robot relations.
-
D.
Aurora
Aurora is a major suburban city in the Denver metropolitan area of Colorado, known for its diverse population, extensive parks and open spaces, and role as a key economic and residential hub on the eastern side of the metro region.
-
E.
Aurora
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots like Baler, and lush mountainous landscapes.
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa007344819099515fda367f7016 |
completed | March 9, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5403235408190b47b8d4f4e21d094 |
completed | March 14, 2026, 11:02 a.m. |
Created at: March 9, 2026, 3:33 p.m.