Triple
T2529291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Tilley |
E56116
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Tilley
Tilley is an English surname of Norman origin that has been borne by various notable figures, including early American colonists.
|
E275447
|
NE FINISHED |
How this triple was built (4 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: Tilley | Statement: [Edward Tilley, familyName, Tilley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tilley Context triple: [Edward Tilley, familyName, Tilley]
-
A.
Wylie
Wylie is a suburban city in the Dallas–Fort Worth metropolitan area in northeastern Texas.
-
B.
Brylin
Brylin is the surname of Sergei Brylin, a former Russian professional ice hockey player and three-time Stanley Cup champion with the New Jersey Devils.
-
C.
Tiltil
Tiltil is a small town and commune in central Chile known for its rural character and historical significance within the Santiago Metropolitan Region.
-
D.
Lilly Belle
Lilly Belle is a steam locomotive that operates on the Walt Disney World Railroad at the Magic Kingdom theme park in Florida.
-
E.
Ellies
The Ellies are annual awards recognizing excellence in magazine journalism and publishing, presented by the American Society of Magazine Editors.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tilley Triple: [Edward Tilley, familyName, Tilley]
Generated description
Tilley is an English surname of Norman origin that has been borne by various notable figures, including early American colonists.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tilley Target entity description: Tilley is an English surname of Norman origin that has been borne by various notable figures, including early American colonists.
-
A.
Wylie
Wylie is a suburban city in the Dallas–Fort Worth metropolitan area in northeastern Texas.
-
B.
Brylin
Brylin is the surname of Sergei Brylin, a former Russian professional ice hockey player and three-time Stanley Cup champion with the New Jersey Devils.
-
C.
Tiltil
Tiltil is a small town and commune in central Chile known for its rural character and historical significance within the Santiago Metropolitan Region.
-
D.
Lilly Belle
Lilly Belle is a steam locomotive that operates on the Walt Disney World Railroad at the Magic Kingdom theme park in Florida.
-
E.
Keely
Keely is a surname most notably associated with Patrick Charles Keely, a prominent 19th-century Irish-American architect known for designing numerous Roman Catholic churches in the United States.
- F. None of above. chosen
Provenance (5 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd25903f08190b46e12d32278daca |
completed | March 7, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2bb6bb608190845706f1a675ad1c |
completed | March 9, 2026, 8:21 p.m. |
| NEDg | Description generation | batch_69af51dee3508190a0d1608a7d905742 |
completed | March 9, 2026, 11:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af52a97d008190b55491fa557eb729 |
completed | March 9, 2026, 11:07 p.m. |
Created at: March 6, 2026, 9:46 p.m.