Triple
T15372086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Haise |
E367572
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Haise |
E367572
|
NE 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: Haise | Statement: [Fred Haise, familyName, Haise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haise Context triple: [Fred Haise, familyName, Haise]
-
A.
Haise
chosen
Haise is the surname of Fred Haise, the American astronaut and Apollo 13 lunar module pilot.
-
B.
Hase
The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
-
C.
Haisyn
Haisyn is a city in central Ukraine known as a local administrative and economic center within Vinnytsia Oblast.
-
D.
Haitink
Haitink is a Dutch surname most famously associated with Bernard Haitink, the renowned 20th-century conductor known for his interpretations of the symphonic repertoire.
-
E.
Haro
Haro is a historic town in Spain’s La Rioja region, renowned for its wineries and annual wine festival.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5c1d548190930bfaf0861595ae |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b528f408190b66d3d6e10e90a43 |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.