Triple
T33138499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nausicaa (Ulysses episode) |
E848075
|
entity |
| Predicate | characterDetail |
P32702
|
FINISHED |
| Object | Gerty MacDowell has a lame leg |
—
|
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: Gerty MacDowell has a lame leg | Statement: [Nausicaa (Ulysses episode), characterDetail, Gerty MacDowell has a lame leg]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterDetail Context triple: [Nausicaa (Ulysses episode), characterDetail, Gerty MacDowell has a lame leg]
-
A.
characterDescription
chosen
Indicates that one entity provides a textual description or portrayal of the characteristics, traits, or attributes of another entity.
-
B.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
C.
characterSetting
Indicates that a character is associated with, appears in, or is situated within a particular setting or environment.
-
D.
characterReveals
Indicates that one character discloses or makes known information, feelings, or intentions to another character.
-
E.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
- 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_69f3495961d88190b16ea542c2c5f825 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff246e0d4481908bcec718e1d4025b |
completed | May 9, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69ff23cb70ac81909b776ace4597ae9c |
completed | May 9, 2026, 12:08 p.m. |
Created at: May 1, 2026, 1:27 a.m.