Triple
T31076711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Thief Who Came to Dinner |
E791982
|
entity |
| Predicate | titleCharacterAlias |
P125094
|
FINISHED |
| Object | Webster |
—
|
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: Webster | Statement: [The Thief Who Came to Dinner, titleCharacterAlias, Webster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleCharacterAlias Context triple: [The Thief Who Came to Dinner, titleCharacterAlias, Webster]
-
A.
characterAlias
Indicates that one character is known or referred to by an alternative name or alias.
-
B.
titleCharacterNamedAfter
Indicates that a work’s title character is named after, or shares their name with, another specific entity.
-
C.
associatedWithCharacterAlias
Indicates that one entity is linked to or connected with an alternative name or alias used for a particular character.
-
D.
titleCharacterRealName
Indicates that a character known by a title or alias has the specified real (personal) name.
-
E.
leadCharacterNickname
chosen
Indicates that one entity is the nickname commonly used for the lead (main) character of another entity.
- 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_69f224ccdbbc81909b0cdb4cc2d70c7a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe189fec148190aeef51b417ba15b0 |
completed | May 8, 2026, 5:08 p.m. |
| PD | Predicate disambiguation | batch_69fe17285b0881908de7569d8dbd20bd |
completed | May 8, 2026, 5:02 p.m. |
Created at: April 29, 2026, 9:02 p.m.