Triple
T4916086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Squire Christopher Dale |
E110351
|
entity |
| Predicate | relationshipToLilyDale |
P59981
|
FINISHED |
| Object | uncle and guardian-like figure |
—
|
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: uncle and guardian-like figure | Statement: [Squire Christopher Dale, relationshipToLilyDale, uncle and guardian-like figure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLilyDale Context triple: [Squire Christopher Dale, relationshipToLilyDale, uncle and guardian-like figure]
-
A.
relationshipToHillHouse
Indicates the type or nature of a subject’s relationship or connection to Hill House.
-
B.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
C.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
-
D.
relationshipToSaintBarbara
Indicates that one entity has a specified relationship or connection to Saint Barbara.
-
E.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
- 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_69bd44132b94819088522d92beaadc78 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6fa559608190aa8d02b1547b8f97 |
completed | March 20, 2026, 4:02 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3421588190ab08e92b9558042e |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd6e482984819087124216738f1e29 |
completed | March 20, 2026, 3:56 p.m. |
Created at: March 20, 2026, 1:29 p.m.