Triple
T27321250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Fagan |
E689500
|
entity |
| Predicate | relationshipStatusWithHelenKeller |
P199780
|
FINISHED |
| Object | engagement opposed by Keller’s family |
—
|
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: engagement opposed by Keller’s family | Statement: [Peter Fagan, relationshipStatusWithHelenKeller, engagement opposed by Keller’s family]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWithHelenKeller Context triple: [Peter Fagan, relationshipStatusWithHelenKeller, engagement opposed by Keller’s family]
-
A.
relationshipWithHelene
Indicates a relationship or connection that an entity has with Helene.
-
B.
relationshipStatusWithHelenaPeabody
Indicates the nature or state of a subject’s personal or romantic relationship with Helena Peabody.
-
C.
relationshipToHolden
Indicates the type or nature of a person's relational connection to Holden.
-
D.
relationshipToKenny
Indicates the specific familial, social, or interpersonal connection that one entity has to Kenny.
-
E.
relationshipToSheilaGreene
Indicates the specific type of relationship or connection an entity has to Sheila Greene.
- 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_69ef355c53a08190a8a92e355a7ce115 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69ff5803c02c81908b63067119f5e684 |
completed | May 9, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69ff576d8b308190b49a1e072a0ae661 |
completed | May 9, 2026, 3:49 p.m. |
| PDg | Predicate description generation | batch_69ff58031bb081909fea985b16f69da4 |
completed | May 9, 2026, 3:51 p.m. |
Created at: April 27, 2026, 11:33 a.m.