Triple
T21993530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Book |
E543146
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Fu Works
Fu Works is a Dutch film production company known for producing acclaimed feature films and documentaries, including the World War II drama "Black Book."
|
E1512534
|
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: Fu Works | Statement: [Black Book, productionCompany, Fu Works]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fu Works Context triple: [Black Book, productionCompany, Fu Works]
-
A.
FUNO
FUNO is the stock ticker symbol for Fibra Uno, one of Mexico’s largest real estate investment trusts (REITs) focused on commercial properties.
-
B.
Funka
Funka is a small village in northern Poland known for its scenic lakeside setting and recreational access to Lake Charzykowskie.
-
C.
Fu
Fu is a Chinese surname borne by various notable historical and contemporary figures across politics, arts, and other fields.
-
D.
FUK
FUK is the IATA airport code for Fukuoka Airport, a major international and domestic air hub serving the city of Fukuoka in Japan.
-
E.
UR FUN
UR FUN is a synth-pop and indie rock album by the American band of Montreal, noted for its upbeat, hook-driven songs and emotionally candid lyrics.
- 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: Fu Works Triple: [Black Book, productionCompany, Fu Works]
Generated description
Fu Works is a Dutch film production company known for producing acclaimed feature films and documentaries, including the World War II drama "Black Book."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fu Works Target entity description: Fu Works is a Dutch film production company known for producing acclaimed feature films and documentaries, including the World War II drama "Black Book."
-
A.
FUNO
FUNO is the stock ticker symbol for Fibra Uno, one of Mexico’s largest real estate investment trusts (REITs) focused on commercial properties.
-
B.
Funka
Funka is a small village in northern Poland known for its scenic lakeside setting and recreational access to Lake Charzykowskie.
-
C.
Fu
Fu is a Chinese surname borne by various notable historical and contemporary figures across politics, arts, and other fields.
-
D.
FUK
FUK is the IATA airport code for Fukuoka Airport, a major international and domestic air hub serving the city of Fukuoka in Japan.
-
E.
UR FUN
UR FUN is a synth-pop and indie rock album by the American band of Montreal, noted for its upbeat, hook-driven songs and emotionally candid lyrics.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1270f77fc8190aadcc02760d65ac0 |
completed | April 28, 2026, 9:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a6d85f104819088dba8d0bf347fbe |
completed | May 18, 2026, 1:38 a.m. |
| NEDg | Description generation | batch_6a0a6e556f3c8190927d1c0cba23463c |
completed | May 18, 2026, 1:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a6f3de2e88190824c9cc266c7ee8c |
completed | May 18, 2026, 1:45 a.m. |
Created at: April 16, 2026, 8:17 p.m.