Triple
T12049276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Willis |
E286870
|
entity |
| Predicate | hasFirstName |
P17
|
FINISHED |
| Object | Helen |
unclear NED1
|
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: Helen | Statement: [Helen Willis, hasFirstName, Helen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helen Context triple: [Helen Willis, hasFirstName, Helen]
-
A.
Helen
Helen is a central character in Ernest Hemingway’s short story “The Snows of Kilimanjaro,” portrayed as the wealthy, devoted wife and companion of the writer Harry during his final, reflective days in Africa.
-
B.
Helen
Helen is the given name of H. T. Lowe-Porter, the American translator best known for bringing Thomas Mann’s works into English.
-
C.
Helen
Helen is the daring, quick-thinking heroine of the early 20th-century silent film serial "The Hazards of Helen," known for her action-packed, stunt-filled adventures.
-
D.
Helen
Helen is a figure from Greek mythology famed for her extraordinary beauty, whose abduction by Paris sparked the Trojan War.
-
E.
Helen
Helen is a central survivor and maternal figure in the post-apocalyptic film "Waterworld," known for her determination to protect the child Enola and seek the mythical Dryland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904227958819084dbd5eb2566c735 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49dd140a48190844f64c228e6367a |
completed | May 1, 2026, 12:34 p.m. |
Created at: April 8, 2026, 9:47 p.m.