Triple
T201163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fay Wray |
E4506
|
entity |
| Predicate | notableRole |
P22
|
FINISHED |
| Object |
Ann Darrow
Ann Darrow is the fictional damsel-in-distress heroine best known as the female lead in the classic giant-ape film "King Kong."
|
E4506
|
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: Ann Darrow | Statement: [Fay Wray, notableRole, Ann Darrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Darrow Context triple: [Fay Wray, notableRole, Ann Darrow]
-
A.
Fay Wray
Fay Wray was a Canadian-American actress best known for her iconic role as the damsel Ann Darrow in the classic 1933 film "King Kong."
-
B.
Vivian Lake Brady
Vivian Lake Brady is the daughter of NFL quarterback Tom Brady and supermodel Gisele Bündchen.
-
C.
Mary Lee Woods
Mary Lee Woods was a British mathematician and computer scientist who worked on early computers at Ferranti and was the mother of World Wide Web inventor Tim Berners-Lee.
-
D.
Elsa Lanchester
Elsa Lanchester was a British-born character actress best known for her eccentric and memorable roles in classic Hollywood films, including her iconic turn in "The Bride of Frankenstein."
-
E.
Cecilia DeMille Harper
Cecilia DeMille Harper was the daughter of legendary American film director and producer Cecil B. DeMille.
- 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: Ann Darrow Triple: [Fay Wray, notableRole, Ann Darrow]
Generated description
Ann Darrow is the fictional damsel-in-distress heroine best known as the female lead in the classic giant-ape film "King Kong."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ann Darrow Target entity description: Ann Darrow is the fictional damsel-in-distress heroine best known as the female lead in the classic giant-ape film "King Kong."
-
A.
Fay Wray
chosen
Fay Wray was a Canadian-American actress best known for her iconic role as the damsel Ann Darrow in the classic 1933 film "King Kong."
-
B.
Vivian Lake Brady
Vivian Lake Brady is the daughter of NFL quarterback Tom Brady and supermodel Gisele Bündchen.
-
C.
Mary Lee Woods
Mary Lee Woods was a British mathematician and computer scientist who worked on early computers at Ferranti and was the mother of World Wide Web inventor Tim Berners-Lee.
-
D.
Elsa Lanchester
Elsa Lanchester was a British-born character actress best known for her eccentric and memorable roles in classic Hollywood films, including her iconic turn in "The Bride of Frankenstein."
-
E.
Cecilia DeMille Harper
Cecilia DeMille Harper was the daughter of legendary American film director and producer Cecil B. DeMille.
- F. None of above.
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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25be5a6d081909723b23a6361d6ea |
completed | Feb. 28, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3672e822c8190be0d6c0714034ff7 |
completed | Feb. 28, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69a367bc6cb48190b5bc588db0833474 |
completed | Feb. 28, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3681d99a881908ea8d2632ba2aab1 |
completed | Feb. 28, 2026, 10:11 p.m. |
Created at: Feb. 28, 2026, 2:51 a.m.