Triple
T13482430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Fujitsubo |
E318403
|
entity |
| Predicate | relationshipToLadyKiritsubo |
P110565
|
FINISHED |
| Object | look-alike |
—
|
LITERAL 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: look-alike | Statement: [Lady Fujitsubo, relationshipToLadyKiritsubo, look-alike]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLadyKiritsubo Context triple: [Lady Fujitsubo, relationshipToLadyKiritsubo, look-alike]
-
A.
relationshipTypeWithToruWatanabe
Indicates the specific nature or category of relationship that an entity has with Toru Watanabe.
-
B.
relationshipToOdette
Indicates the specific familial, social, or interpersonal connection that an entity has with Odette.
-
C.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
D.
relationshipToKittyBennet
Indicates the specific type of personal or familial connection an entity has to Kitty Bennet.
-
E.
relationshipToTopa
Indicates a familial or social relationship that an entity has specifically with Topa.
- F. None of above. chosen
Provenance (4 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3868ec8190a6a1803018d4f2d8 |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae06061881909a6a6032e0507587 |
completed | April 12, 2026, 2:36 p.m. |
| PDg | Predicate description generation | batch_69dbaecc98cc8190829f5be759c4f1e3 |
completed | April 12, 2026, 2:40 p.m. |
Created at: April 9, 2026, 9:42 p.m.