Triple
T29498415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanley Reames |
E748301
|
entity |
| Predicate | spouseMarriageDurationWithJanetLeigh |
P185208
|
FINISHED |
| Object | brief marriage |
—
|
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: brief marriage | Statement: [Stanley Reames, spouseMarriageDurationWithJanetLeigh, brief marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseMarriageDurationWithJanetLeigh Context triple: [Stanley Reames, spouseMarriageDurationWithJanetLeigh, brief marriage]
-
A.
spouseOrderRelativeToJanetLeigh
Indicates the position or sequence of a person’s spouse relative to Janet Leigh’s spouses (e.g., earlier or later in marital order).
-
B.
marriageDurationWithJackLemmon
Indicates the length of time an entity was married to Jack Lemmon.
-
C.
marriageDurationWithBelaLugosi
Indicates the length of time an entity was married to Bela Lugosi.
-
D.
marriageDurationWithRockHudson
Indicates the length of time an entity was married to Rock Hudson.
-
E.
marriageDurationWithYulBrynner
Indicates the length of time that an entity was married to Yul Brynner.
- 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_69f0bd448c6881908aa6b475cefd5ddc |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69f7bbf812cc8190a16917c5daaff2df |
completed | May 3, 2026, 9:19 p.m. |
Created at: April 28, 2026, 4:21 p.m.