Triple
T35557475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ian Miller |
E1027537
|
entity |
| Predicate | relationshipToToulaPortokalos |
P207046
|
FINISHED |
| Object | husband |
—
|
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: husband | Statement: [Ian Miller, relationshipToToulaPortokalos, husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToToulaPortokalos Context triple: [Ian Miller, relationshipToToulaPortokalos, husband]
-
A.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
B.
relationshipToTina
Indicates the specific type of personal or social relationship that an entity has with Tina.
-
C.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
-
D.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
E.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
- 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_69f76e014fd481909e9f04ac603a2aa9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:04 p.m.