Triple
T1839894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiss Me Deadly |
E41150
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
|
E211461
|
NE FINISHED |
How this triple was built (4 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: Velda | Statement: [Kiss Me Deadly, character, Velda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Velda Context triple: [Kiss Me Deadly, character, Velda]
-
A.
Freirina
Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
-
B.
Huelén
Huelén is the former indigenous name for Cerro Santa Lucía, a historic hill and urban park in central Santiago, Chile.
-
C.
Valbo
Valbo is a locality in Gävleborg County, Sweden, known as the hometown of NHL ice hockey star Nicklas Bäckström.
-
D.
Velkua
Velkua is a former island municipality in southwestern Finland known for its coastal archipelago landscape in the Baltic Sea.
-
E.
Tynaarlo
Tynaarlo is a municipality in the northeastern Netherlands known for its rural character and location between the cities of Groningen and Assen.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Velda Triple: [Kiss Me Deadly, character, Velda]
Generated description
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Velda Target entity description: Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
A.
Freirina
Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
-
B.
Huelén
Huelén is the former indigenous name for Cerro Santa Lucía, a historic hill and urban park in central Santiago, Chile.
-
C.
Valbo
Valbo is a locality in Gävleborg County, Sweden, known as the hometown of NHL ice hockey star Nicklas Bäckström.
-
D.
Velkua
Velkua is a former island municipality in southwestern Finland known for its coastal archipelago landscape in the Baltic Sea.
-
E.
Tynaarlo
Tynaarlo is a municipality in the northeastern Netherlands known for its rural character and location between the cities of Groningen and Assen.
- F. None of above. chosen
Provenance (5 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb03b3eb08190ae68d8476fc89c7f |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adead8e9148190b7cba0f325dc58c4 |
completed | March 8, 2026, 9:32 p.m. |
| NEDg | Description generation | batch_69adeb6e8fe08190a4732d42aa15ee8e |
completed | March 8, 2026, 9:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adebea03a08190bd055e3e6460b5f4 |
completed | March 8, 2026, 9:36 p.m. |
Created at: March 4, 2026, 7:33 p.m.