Triple
T10481774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Bates |
E247188
|
entity |
| Predicate | relationshipToJackBrown |
P94549
|
FINISHED |
| Object | employer |
—
|
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: employer | Statement: [U.S. Bates, relationshipToJackBrown, employer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJackBrown Context triple: [U.S. Bates, relationshipToJackBrown, employer]
-
A.
relationshipToJamesBrown
Indicates the type of personal, professional, or familial relationship that an entity has with James Brown.
-
B.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
C.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
D.
hasRelationshipToJackTorrance
Indicates that one entity has some form of relationship or connection to Jack Torrance.
-
E.
relationshipToQuentinJacobsen
Indicates the specific type of relationship or connection an entity has to Quentin Jacobsen.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5095c5dc88190902582db28df01b4 |
completed | April 7, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d4fb8a30848190b33cf43f005a028e |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d5092af880819082b42c0a68e45c5f |
completed | April 7, 2026, 1:39 p.m. |
Created at: April 6, 2026, 12:22 p.m.