Triple
T38503691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An Abundance of Katherines |
E919905
|
entity |
| Predicate | numberOfKatherinesDatedByProtagonist |
P204696
|
FINISHED |
| Object | 19 |
—
|
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: 19 | Statement: [An Abundance of Katherines, numberOfKatherinesDatedByProtagonist, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfKatherinesDatedByProtagonist Context triple: [An Abundance of Katherines, numberOfKatherinesDatedByProtagonist, 19]
-
A.
hasOnScreenKissWith
Indicates that two entities share a romantic or affectionate kiss depicted visually within the same on-screen scene.
-
B.
isRomanticLeadOf
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
-
C.
numberOfHusbandsKilled
Indicates the count of husbands that an entity has killed.
-
D.
relationshipTypeWithFemaleLead
Indicates the type or nature of a relationship that an entity has with a female lead.
-
E.
hasNumberOfPrincesses
Indicates the specific count of princesses associated with a given entity.
- 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_69f76e9ddd4481908f8c04439d848f9d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:31 p.m.