Triple
T28229115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unlucky 13 |
E711673
|
entity |
| Predicate | seriesNumberInWomen’sMurderClub |
P175232
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [Unlucky 13, seriesNumberInWomen’sMurderClub, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seriesNumberInWomen’sMurderClub Context triple: [Unlucky 13, seriesNumberInWomen’sMurderClub, 13]
-
A.
mysterySet
Indicates that an entity belongs to a collection or group whose nature, contents, or defining criteria are unknown or intentionally unspecified.
-
B.
mysteryAssociatedWith
Indicates a relationship where something is connected to, involved in, or characterized by an element of mystery or the unknown.
-
C.
murderedIn
Indicates that one entity unlawfully killed another entity at or within a specified location.
-
D.
numberOfMysteries
Indicates the quantity or count of mysteries associated with a given entity.
-
E.
containsMurderMystery
Indicates that one entity (such as a work or collection) includes or features a murder mystery as part of its content.
- 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_69efb51dfb048190ada79b745c33b363 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6cee547108190ad3bc84297d8f516 |
completed | May 3, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1188708190b8f0f56e595e6057 |
completed | May 3, 2026, 4:16 a.m. |
| PDg | Predicate description generation | batch_69f6cee3604c81908a07eade2f39064e |
completed | May 3, 2026, 4:28 a.m. |
Created at: April 27, 2026, 10:51 p.m.