Triple
T26877503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Megan Hipwell |
E676790
|
entity |
| Predicate | relationshipWithRachelWatson |
P67789
|
FINISHED |
| Object | indirectly connected through Tom Watson |
—
|
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: indirectly connected through Tom Watson | Statement: [Megan Hipwell, relationshipWithRachelWatson, indirectly connected through Tom Watson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithRachelWatson Context triple: [Megan Hipwell, relationshipWithRachelWatson, indirectly connected through Tom Watson]
-
A.
relationshipToRachelWatson
chosen
Indicates the specific type of relationship or connection an entity has to Rachel Watson.
-
B.
relationshipTypeWithRachelKeller
Indicates the specific nature or category of relationship that an entity has with Rachel Keller.
-
C.
relationshipTypeWithRuth Levinson
Indicates the specific nature or category of relationship that an entity has with Ruth Levinson.
-
D.
relationshipTypeWithMonicaWright
Indicates the specific nature or category of the relationship that an entity has with Monica Wright.
-
E.
relationshipTypeWithCallieSadecki
Indicates the specific nature or category of relationship that an entity has with Callie Sadecki.
- 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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69fefb15220081908da36aac386fa582 |
completed | May 9, 2026, 9:15 a.m. |
| PD | Predicate disambiguation | batch_69fefa8e8ad48190a723fed81e9d64d0 |
completed | May 9, 2026, 9:12 a.m. |
Created at: April 27, 2026, 5:36 a.m.