Triple
T9329732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julie Andre |
E224484
|
entity |
| Predicate | relationshipToJervisPendletonIII |
P87549
|
FINISHED |
| Object | ward |
—
|
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: ward | Statement: [Julie Andre, relationshipToJervisPendletonIII, ward]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJervisPendletonIII Context triple: [Julie Andre, relationshipToJervisPendletonIII, ward]
-
A.
termRelationToPresident
Indicates the nature of a person’s connection or role in relation to a president, such as their position, association, or involvement with that president.
-
B.
relationshipToHarveyCheyneJr
Indicates the specific familial, social, or professional relationship that an entity has to Harvey Cheyne Jr.
-
C.
hasPoliticalRelationshipWith
Indicates a political connection or association between two entities, such as alliances, rivalries, collaborations, or other forms of political interaction.
-
D.
relationshipToGovernor
Indicates the specific familial, professional, or social relationship that one entity has to a governor.
-
E.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37acbc04819092a67d7f392c74cd |
completed | April 1, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:39 p.m.