Triple
T31910666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Part of a Long Story |
E814673
|
entity |
| Predicate | hasAuthorSpouseSubject |
P70306
|
FINISHED |
| Object | Eugene O’Neill |
—
|
NE NERFINISHED |
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: Eugene O’Neill | Statement: [Part of a Long Story, hasAuthorSpouseSubject, Eugene O’Neill]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorSpouseSubject Context triple: [Part of a Long Story, hasAuthorSpouseSubject, Eugene O’Neill]
-
A.
hasAuthorSpouse
chosen
Indicates that the spouse of the subject entity is the author of the related work or entity.
-
B.
hasAuthorMarriedName
Indicates that an author’s married surname or full married name is associated with them, typically differing from their birth or maiden name.
-
C.
authorSpouseOrigin
Indicates that the spouse of the author comes from or is originally associated with a specified place or origin.
-
D.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
E.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
- F. None of above.
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_69f348f109d88190b5005372c53d2fcd |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 1, 2026, 12:01 a.m.