Triple
T29309778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Isaacs |
E743200
|
entity |
| Predicate | spouseOrderForEvaGabor |
P4764
|
FINISHED |
| Object | third 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: third husband | Statement: [Charles Isaacs, spouseOrderForEvaGabor, third husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrderForEvaGabor Context triple: [Charles Isaacs, spouseOrderForEvaGabor, third husband]
-
A.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
marriageOrderWithBelaLugosi
Indicates the chronological order in which an entity entered into marriage with Bela Lugosi.
-
C.
marriageOrderToBelaLugosi
Indicates the sequence or ranking of a person’s marriage in relation to their marriage to Bela Lugosi.
-
D.
motherSpouseOrder
Indicates that the subject is the spouse of the object’s mother, with an ordering or ranking among multiple such spouses.
-
E.
spouseOrderRelativeToGeneRoddenberry
Indicates the position or sequence of a person among Gene Roddenberry’s spouses (e.g., first spouse, second spouse, etc.).
- 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_69f09123ed9881909f351f7541933f5e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: April 28, 2026, 1:16 p.m.